Assignment 16. Malaria does not affect the red cell count in the lizards.

Size: px
Start display at page:

Download "Assignment 16. Malaria does not affect the red cell count in the lizards."

Transcription

1 ignmen If he null hypohei i no rejeced ha he wo ample are differen, hen he Type of Error would be ype II Fale. The cieni rejeced baed on a bad calculaion, no baed upon ample ha yielded an exremely large value of he e aiic No. large p-value indicae ha here i no ufficien evidence o uppor ha here i a difference beween he wo reamen for he mean yolic volume for he populaion. I could be ha here wa no ufficien evidence o deermine he difference beween he wo reamen due o he ample ize ued a. umpion. Normal Populaion or large ample The null hypohei i : 1 R 1 0 Malaria doe no affec he red cell coun in he lizard. The alernaive hypohei : 1 R 1 0 Malaria reduce he red cell coun in he lizard.. STEP : = df p - value p - value he p - value o he ignifican 0.05, we do no rejec 0.05 Wih a p-value>0.5, we do no rejec he null hypohei. There i no ufficien evidence o conclude ha malaria paien have a lower red blood coun.

2 b. a. umpion. Normal Populaion or large ample The null hypohei i : 1 R 1 0 Malaria doe no affec he red cell coun in he lizard. The alernaive hypohei : 1 R 1 0 Malaria reduce he red cell coun in he lizard.. STEP : = df p - value p - value he p - value o he ignifican , we do no rejec 0.10 Wih a p-value>0.5, we do no rejec he null hypohei. There i no ufficien evidence o conclude ha malaria paien have a lower red blood coun a.. umpion. Normal Populaion or large ample

3 : 1 R he were o be hypnoized breahing paern i he ame for he in comparion o he conrol group male ha STEP : =0.05 : 1 R he breahing paern i he differen for he were o be hypnoized in comparion o he conrol group male ha SE y y Sep 4 wih df ince , he p - value Sep 5 he p - value o he ignifican he p - value , we rejec 0.05 Sep 6 Wih p - value 0.0, he null hypohei i rejeced. The mean breahing paern beween h e male o be hypnoized b. umpion. Normal Populaion or large ample he conrol group i differen. : 1 R he were o be hypnoized breahing paern i he ame in comparion o he conrol for he group male ha : 1 R he breahing rae i greaer for he male were o be hypnoized in comparion o he conrol group ha STEP : =0.05

4 SE y y Sep 4 wih df ince , he p - value Sep 5 he he p - value o he ignifican p - value , we rejec Sep 6 Since he p - value, he null hypohei i rejeced. The mean breahing rae beween h e male o be hypnoized i greaer h an he conrol group. c. The direcional e i more appropriae ince we are inereed in knowing how he wo group are differen. Thi would only be appropriae if he experimener had prior o he experimen had hypoheized ha he breahing rae would be increaed for he group o be hypnoized : 1 R he ex of he older ibling ha no effec on he weigh of he younger ibling : 1 R he mean weigh of younger mean weigh of younger ibling of female ibling of male i le han he SE y y he p - value wih df ince , he p - value he p - value o he ignifican , we do rejec From hi udy ample ize, he mean birhweigh of children following a male birh i le han he mean birhweigh of hoe following female birh.

5 Supplemenary Exercie 7.S. umpion. Normal Populaion or large ample The null hypohei i : 1 R 1 0 The free calcium concenraion in he blood plaele i he ame for people wih normal blood preure wih high blood preure. The alernaive hypohei : 1 R 1 0 The free calcium concenraion in he blood plaele i differen for people wih normal blood preure for hoe wih high blood preure. STEP : = df p - value he p - value p - value o he ignifican, we rejec Wih a p - value , we do rejec he null hypohei. There i ufficien evidence o conclude ha free calcium concenraion in he blood plaele i differen for people wih normal blood preure hoe wih high blood preure. 7.S.4. I i ill valid becaue of he large ample ize. 7.S.1 a. Fale, ince he confidence inerval give u a range ha we infer cover 1 -.I doe no ell u where he bulk of he daa lie b. True, hi i wha a confidence inerval ell u. c. Fale, he confidence ell u abou he difference beween 1 - ; i doe no ell u abou fuure value for he difference beween y1 y.

6 d. Fale, The confidence inerval i ued o make an inference abou he difference beween no ell u abou individual daa poin uch a he number of calorie in a paricular enree. 1. I doe 7.S.4 a. umpion. Normal Populaion or large ample The null hypohei i : 1 R 1 0 The ue of he eroid roenedione (ro) ha no effec on la pull down rengh. The alernaive hypohei : 1 R 1 0 The ue of he eroid roenedione (ro) ha an effec on la pull down rengh. STEP : = df p - value he p - value o he ignifican 0. p - value , we do no rejec Wih a 0. p - value 0. 4, we do no rejec he null hypohei. There i no ufficien evidence o conclude ha he mean la pull down rengh i differen for hoe ha ook ro hoe ha ook he placebo. b. umpion. Normal Populaion or large ample The null hypohei i : 1 R 1 0 The ue of he eroid roenedione (ro) ha no effec on la pull down rengh.

7 The alernaive hypohei : 1 R 1 0 The ue of he eroid roenedione (ro) ha increae la pull down rengh. STEP : = df p - value 0.5. We expec he e aiic o be poiive from our alernaive hypohei, bu he e aiic i in he oppoie direcion. he p - value o he ignifican p - value , we do no rejec Wih a p - value 0. 5, we do no rejec he null hypohei. There i no ufficien evidence o conclude ha he mean la pull down rengh i improved for hoe ha ook ro over hoe ha ook he placebo.

Stat13 Homework 7. Suggested Solutions

Stat13 Homework 7. Suggested Solutions Sa3 Homework 7 hp://www.a.ucla.edu/~dinov/coure_uden.hml Suggeed Soluion Queion 7.50 Le denoe infeced and denoe noninfeced. H 0 : Malaria doe no affec red cell coun (µ µ ) H A : Malaria reduce red cell

More information

Review - Week 10. There are two types of errors one can make when performing significance tests:

Review - Week 10. There are two types of errors one can make when performing significance tests: Review - Week Read: Chaper -3 Review: There are wo ype of error oe ca make whe performig igificace e: Type I error The ull hypohei i rue, bu we miakely rejec i (Fale poiive) Type II error The ull hypohei

More information

Comparing Means: t-tests for One Sample & Two Related Samples

Comparing Means: t-tests for One Sample & Two Related Samples Comparing Means: -Tess for One Sample & Two Relaed Samples Using he z-tes: Assumpions -Tess for One Sample & Two Relaed Samples The z-es (of a sample mean agains a populaion mean) is based on he assumpion

More information

Biol. 356 Lab 8. Mortality, Recruitment, and Migration Rates

Biol. 356 Lab 8. Mortality, Recruitment, and Migration Rates Biol. 356 Lab 8. Moraliy, Recruimen, and Migraion Raes (modified from Cox, 00, General Ecology Lab Manual, McGraw Hill) Las week we esimaed populaion size hrough several mehods. One assumpion of all hese

More information

Hypothesis Testing in the Classical Normal Linear Regression Model. 1. Components of Hypothesis Tests

Hypothesis Testing in the Classical Normal Linear Regression Model. 1. Components of Hypothesis Tests ECONOMICS 35* -- NOTE 8 M.G. Abbo ECON 35* -- NOTE 8 Hypohesis Tesing in he Classical Normal Linear Regression Model. Componens of Hypohesis Tess. A esable hypohesis, which consiss of wo pars: Par : a

More information

Problem Set If all directed edges in a network have distinct capacities, then there is a unique maximum flow.

Problem Set If all directed edges in a network have distinct capacities, then there is a unique maximum flow. CSE 202: Deign and Analyi of Algorihm Winer 2013 Problem Se 3 Inrucor: Kamalika Chaudhuri Due on: Tue. Feb 26, 2013 Inrucion For your proof, you may ue any lower bound, algorihm or daa rucure from he ex

More information

u(t) Figure 1. Open loop control system

u(t) Figure 1. Open loop control system Open loop conrol v cloed loop feedbac conrol The nex wo figure preen he rucure of open loop and feedbac conrol yem Figure how an open loop conrol yem whoe funcion i o caue he oupu y o follow he reference

More information

A First Course on Kinetics and Reaction Engineering. Class 19 on Unit 18

A First Course on Kinetics and Reaction Engineering. Class 19 on Unit 18 A Firs ourse on Kineics and Reacion Engineering lass 19 on Uni 18 Par I - hemical Reacions Par II - hemical Reacion Kineics Where We re Going Par III - hemical Reacion Engineering A. Ideal Reacors B. Perfecly

More information

13.1 Accelerating Objects

13.1 Accelerating Objects 13.1 Acceleraing Objec A you learned in Chaper 12, when you are ravelling a a conan peed in a raigh line, you have uniform moion. However, mo objec do no ravel a conan peed in a raigh line o hey do no

More information

Topic 3. Single factor ANOVA [ST&D Ch. 7]

Topic 3. Single factor ANOVA [ST&D Ch. 7] Topic 3. Single facor ANOVA [ST&D Ch. 7] "The analyi of variance i more han a echnique for aiical analyi. Once i i underood, ANOVA i a ool ha can provide an inigh ino he naure of variaion of naural even"

More information

Chapter 15. Time Series: Descriptive Analyses, Models, and Forecasting

Chapter 15. Time Series: Descriptive Analyses, Models, and Forecasting Chaper 15 Time Series: Descripive Analyses, Models, and Forecasing Descripive Analysis: Index Numbers Index Number a number ha measures he change in a variable over ime relaive o he value of he variable

More information

Randomized Perfect Bipartite Matching

Randomized Perfect Bipartite Matching Inenive Algorihm Lecure 24 Randomized Perfec Biparie Maching Lecurer: Daniel A. Spielman April 9, 208 24. Inroducion We explain a randomized algorihm by Ahih Goel, Michael Kapralov and Sanjeev Khanna for

More information

3.1 More on model selection

3.1 More on model selection 3. More on Model selecion 3. Comparing models AIC, BIC, Adjused R squared. 3. Over Fiing problem. 3.3 Sample spliing. 3. More on model selecion crieria Ofen afer model fiing you are lef wih a handful of

More information

STATISTICAL MODELLING PRACTICAL VI SOLUTIONS

STATISTICAL MODELLING PRACTICAL VI SOLUTIONS VI-1. TATITICAL MODELLING PRACTICAL VI OLUTION VI.1 The following daa are from a Lain square experimen designed o invesigae he moisure conen of urnip greens. The experimen involved he measuremen of he

More information

Nature Neuroscience: doi: /nn Supplementary Figure 1. Spike-count autocorrelations in time.

Nature Neuroscience: doi: /nn Supplementary Figure 1. Spike-count autocorrelations in time. Supplemenary Figure 1 Spike-coun auocorrelaions in ime. Normalized auocorrelaion marices are shown for each area in a daase. The marix shows he mean correlaion of he spike coun in each ime bin wih he spike

More information

Econ107 Applied Econometrics Topic 7: Multicollinearity (Studenmund, Chapter 8)

Econ107 Applied Econometrics Topic 7: Multicollinearity (Studenmund, Chapter 8) I. Definiions and Problems A. Perfec Mulicollineariy Econ7 Applied Economerics Topic 7: Mulicollineariy (Sudenmund, Chaper 8) Definiion: Perfec mulicollineariy exiss in a following K-variable regression

More information

Math 10B: Mock Mid II. April 13, 2016

Math 10B: Mock Mid II. April 13, 2016 Name: Soluions Mah 10B: Mock Mid II April 13, 016 1. ( poins) Sae, wih jusificaion, wheher he following saemens are rue or false. (a) If a 3 3 marix A saisfies A 3 A = 0, hen i canno be inverible. True.

More information

Group B Human

Group B Human /9/009 -Tes for wo independen samples aisical Tess A sep-by-sep guide Is here a significan difference beween he abiliies of rained homing pigeons o locae survivors a sea and he abiliies of rained human

More information

The average rate of change between two points on a function is d t

The average rate of change between two points on a function is d t SM Dae: Secion: Objecive: The average rae of change beween wo poins on a funcion is d. For example, if he funcion ( ) represens he disance in miles ha a car has raveled afer hours, hen finding he slope

More information

2) Of the following questions, which ones are thermodynamic, rather than kinetic concepts?

2) Of the following questions, which ones are thermodynamic, rather than kinetic concepts? AP Chemisry Tes (Chaper 12) Muliple Choice (40%) 1) Which of he following is a kineic quaniy? A) Enhalpy B) Inernal Energy C) Gibb s free energy D) Enropy E) Rae of reacion 2) Of he following quesions,

More information

Math 1b. Calculus, Series, and Differential Equations. Final Exam Solutions

Math 1b. Calculus, Series, and Differential Equations. Final Exam Solutions Mah b. Calculus, Series, and Differenial Equaions. Final Exam Soluions Spring 6. (9 poins) Evaluae he following inegrals. 5x + 7 (a) (x + )(x + ) dx. (b) (c) x arcan x dx x(ln x) dx Soluion. (a) Using

More information

Explore 2 Proving the Vertical Angles Theorem

Explore 2 Proving the Vertical Angles Theorem Explore 2 Proving he Verical Angles Theorem The conjecure from he Explore abou verical angles can be proven so i can be saed as a heorem. The Verical Angles Theorem If wo angles are verical angles, hen

More information

Age (x) nx lx. Age (x) nx lx dx qx

Age (x) nx lx. Age (x) nx lx dx qx Life Tables Dynamic (horizonal) cohor= cohor followed hrough ime unil all members have died Saic (verical or curren) = one census period (day, season, ec.); only equivalen o dynamic if populaion does no

More information

KINEMATICS IN ONE DIMENSION

KINEMATICS IN ONE DIMENSION KINEMATICS IN ONE DIMENSION PREVIEW Kinemaics is he sudy of how hings move how far (disance and displacemen), how fas (speed and velociy), and how fas ha how fas changes (acceleraion). We say ha an objec

More information

Random Processes 1/24

Random Processes 1/24 Random Processes 1/24 Random Process Oher Names : Random Signal Sochasic Process A Random Process is an exension of he concep of a Random variable (RV) Simples View : A Random Process is a RV ha is a Funcion

More information

Introduction to SLE Lecture Notes

Introduction to SLE Lecture Notes Inroducion o SLE Lecure Noe May 13, 16 - The goal of hi ecion i o find a ufficien condiion of λ for he hull K o be generaed by a imple cure. I urn ou if λ 1 < 4 hen K i generaed by a imple curve. We will

More information

Stat 601 The Design of Experiments

Stat 601 The Design of Experiments Sa 601 The Design of Experimens Yuqing Xu Deparmen of Saisics Universiy of Wisconsin Madison, WI 53706, USA December 1, 2016 Yuqing Xu (UW-Madison) Sa 601 Week 12 December 1, 2016 1 / 17 Lain Squares Definiion

More information

Algorithms and Data Structures 2011/12 Week 9 Solutions (Tues 15th - Fri 18th Nov)

Algorithms and Data Structures 2011/12 Week 9 Solutions (Tues 15th - Fri 18th Nov) Algorihm and Daa Srucure 2011/ Week Soluion (Tue 15h - Fri 18h No) 1. Queion: e are gien 11/16 / 15/20 8/13 0/ 1/ / 11/1 / / To queion: (a) Find a pair of ube X, Y V uch ha f(x, Y) = f(v X, Y). (b) Find

More information

Q2.1 This is the x t graph of the motion of a particle. Of the four points P, Q, R, and S, the velocity v x is greatest (most positive) at

Q2.1 This is the x t graph of the motion of a particle. Of the four points P, Q, R, and S, the velocity v x is greatest (most positive) at Q2.1 This is he x graph of he moion of a paricle. Of he four poins P, Q, R, and S, he velociy is greaes (mos posiive) a A. poin P. B. poin Q. C. poin R. D. poin S. E. no enough informaion in he graph o

More information

HYPOTHESIS TESTING. four steps. 1. State the hypothesis and the criterion. 2. Compute the test statistic. 3. Compute the p-value. 4.

HYPOTHESIS TESTING. four steps. 1. State the hypothesis and the criterion. 2. Compute the test statistic. 3. Compute the p-value. 4. Inrodcion o Saisics in Psychology PSY Professor Greg Francis Lecre 24 Hypohesis esing for correlaions Is here a correlaion beween homework and exam grades? for seps. Sae he hypohesis and he crierion 2.

More information

Chapter 13 Homework Answers

Chapter 13 Homework Answers Chaper 3 Homework Answers 3.. The answer is c, doubling he [C] o while keeping he [A] o and [B] o consan. 3.2. a. Since he graph is no linear, here is no way o deermine he reacion order by inspecion. A

More information

SPH3U: Projectiles. Recorder: Manager: Speaker:

SPH3U: Projectiles. Recorder: Manager: Speaker: SPH3U: Projeciles Now i s ime o use our new skills o analyze he moion of a golf ball ha was ossed hrough he air. Le s find ou wha is special abou he moion of a projecile. Recorder: Manager: Speaker: 0

More information

Analyze patterns and relationships. 3. Generate two numerical patterns using AC

Analyze patterns and relationships. 3. Generate two numerical patterns using AC envision ah 2.0 5h Grade ah Curriculum Quarer 1 Quarer 2 Quarer 3 Quarer 4 andards: =ajor =upporing =Addiional Firs 30 Day 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 andards: Operaions and Algebraic Thinking

More information

Bayesian Designs for Michaelis-Menten kinetics

Bayesian Designs for Michaelis-Menten kinetics Bayeian Deign for ichaeli-enen kineic John ahew and Gilly Allcock Deparen of Saiic Univeriy of Newcale upon Tyne.n..ahew@ncl.ac.uk Reference ec. on hp://www.a.ncl.ac.uk/~nn/alk/ile.h Enzyology any biocheical

More information

Vehicle Arrival Models : Headway

Vehicle Arrival Models : Headway Chaper 12 Vehicle Arrival Models : Headway 12.1 Inroducion Modelling arrival of vehicle a secion of road is an imporan sep in raffic flow modelling. I has imporan applicaion in raffic flow simulaion where

More information

ACE 564 Spring Lecture 7. Extensions of The Multiple Regression Model: Dummy Independent Variables. by Professor Scott H.

ACE 564 Spring Lecture 7. Extensions of The Multiple Regression Model: Dummy Independent Variables. by Professor Scott H. ACE 564 Spring 2006 Lecure 7 Exensions of The Muliple Regression Model: Dumm Independen Variables b Professor Sco H. Irwin Readings: Griffihs, Hill and Judge. "Dumm Variables and Varing Coefficien Models

More information

Chapter 15: Phenomena. Chapter 15 Chemical Kinetics. Reaction Rates. Reaction Rates R P. Reaction Rates. Rate Laws

Chapter 15: Phenomena. Chapter 15 Chemical Kinetics. Reaction Rates. Reaction Rates R P. Reaction Rates. Rate Laws Chaper 5: Phenomena Phenomena: The reacion (aq) + B(aq) C(aq) was sudied a wo differen emperaures (98 K and 35 K). For each emperaure he reacion was sared by puing differen concenraions of he 3 species

More information

IB Physics Kinematics Worksheet

IB Physics Kinematics Worksheet IB Physics Kinemaics Workshee Wrie full soluions and noes for muliple choice answers. Do no use a calculaor for muliple choice answers. 1. Which of he following is a correc definiion of average acceleraion?

More information

Algorithmic Discrete Mathematics 6. Exercise Sheet

Algorithmic Discrete Mathematics 6. Exercise Sheet Algorihmic Dicree Mahemaic. Exercie Shee Deparmen of Mahemaic SS 0 PD Dr. Ulf Lorenz 7. and 8. Juni 0 Dipl.-Mah. David Meffer Verion of June, 0 Groupwork Exercie G (Heap-Sor) Ue Heap-Sor wih a min-heap

More information

HW6: MRI Imaging Pulse Sequences (7 Problems for 100 pts)

HW6: MRI Imaging Pulse Sequences (7 Problems for 100 pts) HW6: MRI Imaging Pulse Sequences (7 Problems for 100 ps) GOAL The overall goal of HW6 is o beer undersand pulse sequences for MRI image reconsrucion. OBJECTIVES 1) Design a spin echo pulse sequence o image

More information

Forward guidance. Fed funds target during /15/2017

Forward guidance. Fed funds target during /15/2017 Forward guidance Fed funds arge during 2004 A. A wo-dimensional characerizaion of moneary shocks (Gürkynak, Sack, and Swanson, 2005) B. Odyssean versus Delphic foreign guidance (Campbell e al., 2012) C.

More information

You must fully interpret your results. There is a relationship doesn t cut it. Use the text and, especially, the SPSS Manual for guidance.

You must fully interpret your results. There is a relationship doesn t cut it. Use the text and, especially, the SPSS Manual for guidance. POLI 30D SPRING 2015 LAST ASSIGNMENT TRUMPETS PLEASE!!!!! Due Thursday, December 10 (or sooner), by 7PM hrough TurnIIn I had his all se up in my mind. You would use regression analysis o follow up on your

More information

On Measuring Pro-Poor Growth. 1. On Various Ways of Measuring Pro-Poor Growth: A Short Review of the Literature

On Measuring Pro-Poor Growth. 1. On Various Ways of Measuring Pro-Poor Growth: A Short Review of the Literature On Measuring Pro-Poor Growh 1. On Various Ways of Measuring Pro-Poor Growh: A Shor eview of he Lieraure During he pas en years or so here have been various suggesions concerning he way one should check

More information

6.003 Homework #9 Solutions

6.003 Homework #9 Solutions 6.00 Homework #9 Soluions Problems. Fourier varieies a. Deermine he Fourier series coefficiens of he following signal, which is periodic in 0. x () 0 0 a 0 5 a k sin πk 5 sin πk 5 πk for k 0 a k 0 πk j

More information

What is maximum Likelihood? History Features of ML method Tools used Advantages Disadvantages Evolutionary models

What is maximum Likelihood? History Features of ML method Tools used Advantages Disadvantages Evolutionary models Wha i maximum Likelihood? Hiory Feaure of ML mehod Tool ued Advanage Diadvanage Evoluionary model Maximum likelihood mehod creae all he poible ree conaining he e of organim conidered, and hen ue he aiic

More information

Notes on cointegration of real interest rates and real exchange rates. ρ (2)

Notes on cointegration of real interest rates and real exchange rates. ρ (2) Noe on coinegraion of real inere rae and real exchange rae Charle ngel, Univeriy of Wiconin Le me ar wih he obervaion ha while he lieraure (mo prominenly Meee and Rogoff (988) and dion and Paul (993))

More information

ACE 562 Fall Lecture 5: The Simple Linear Regression Model: Sampling Properties of the Least Squares Estimators. by Professor Scott H.

ACE 562 Fall Lecture 5: The Simple Linear Regression Model: Sampling Properties of the Least Squares Estimators. by Professor Scott H. ACE 56 Fall 005 Lecure 5: he Simple Linear Regression Model: Sampling Properies of he Leas Squares Esimaors by Professor Sco H. Irwin Required Reading: Griffihs, Hill and Judge. "Inference in he Simple

More information

Solutions to Odd Number Exercises in Chapter 6

Solutions to Odd Number Exercises in Chapter 6 1 Soluions o Odd Number Exercises in 6.1 R y eˆ 1.7151 y 6.3 From eˆ ( T K) ˆ R 1 1 SST SST SST (1 R ) 55.36(1.7911) we have, ˆ 6.414 T K ( ) 6.5 y ye ye y e 1 1 Consider he erms e and xe b b x e y e b

More information

Innova Junior College H2 Mathematics JC2 Preliminary Examinations Paper 2 Solutions 0 (*)

Innova Junior College H2 Mathematics JC2 Preliminary Examinations Paper 2 Solutions 0 (*) Soluion 3 x 4x3 x 3 x 0 4x3 x 4x3 x 4x3 x 4x3 x x 3x 3 4x3 x Innova Junior College H Mahemaics JC Preliminary Examinaions Paper Soluions 3x 3 4x 3x 0 4x 3 4x 3 0 (*) 0 0 + + + - 3 3 4 3 3 3 3 Hence x or

More information

The fundamental mass balance equation is ( 1 ) where: I = inputs P = production O = outputs L = losses A = accumulation

The fundamental mass balance equation is ( 1 ) where: I = inputs P = production O = outputs L = losses A = accumulation Hea (iffusion) Equaion erivaion of iffusion Equaion The fundamenal mass balance equaion is I P O L A ( 1 ) where: I inpus P producion O oupus L losses A accumulaion Assume ha no chemical is produced or

More information

Math 333 Problem Set #2 Solution 14 February 2003

Math 333 Problem Set #2 Solution 14 February 2003 Mah 333 Problem Se #2 Soluion 14 February 2003 A1. Solve he iniial value problem dy dx = x2 + e 3x ; 2y 4 y(0) = 1. Soluion: This is separable; we wrie 2y 4 dy = x 2 + e x dx and inegrae o ge The iniial

More information

Appendix to Creating Work Breaks From Available Idleness

Appendix to Creating Work Breaks From Available Idleness Appendix o Creaing Work Breaks From Available Idleness Xu Sun and Ward Whi Deparmen of Indusrial Engineering and Operaions Research, Columbia Universiy, New York, NY, 127; {xs2235,ww24}@columbia.edu Sepember

More information

Errata (1 st Edition)

Errata (1 st Edition) P Sandborn, os Analysis of Elecronic Sysems, s Ediion, orld Scienific, Singapore, 03 Erraa ( s Ediion) S K 05D Page 8 Equaion (7) should be, E 05D E Nu e S K he L appearing in he equaion in he book does

More information

NEWTON S SECOND LAW OF MOTION

NEWTON S SECOND LAW OF MOTION Course and Secion Dae Names NEWTON S SECOND LAW OF MOTION The acceleraion of an objec is defined as he rae of change of elociy. If he elociy changes by an amoun in a ime, hen he aerage acceleraion during

More information

Physics 240: Worksheet 16 Name

Physics 240: Worksheet 16 Name Phyic 4: Workhee 16 Nae Non-unifor circular oion Each of hee proble involve non-unifor circular oion wih a conan α. (1) Obain each of he equaion of oion for non-unifor circular oion under a conan acceleraion,

More information

ANSWERS TO ODD NUMBERED EXERCISES IN CHAPTER

ANSWERS TO ODD NUMBERED EXERCISES IN CHAPTER John Riley 6 December 200 NWER TO ODD NUMBERED EXERCIE IN CHPTER 7 ecion 7 Exercie 7-: m m uppoe ˆ, m=,, M (a For M = 2, i i eay o how ha I implie I From I, for any probabiliy vecor ( p, p 2, 2 2 ˆ ( p,

More information

t = s D Overview of Tests Two-Sample t-test: Independent Samples Independent Samples t-test Difference between Means in a Two-sample Experiment

t = s D Overview of Tests Two-Sample t-test: Independent Samples Independent Samples t-test Difference between Means in a Two-sample Experiment Overview of Te Two-Sample -Te: Idepede Sample Chaper 4 z-te Oe Sample -Te Relaed Sample -Te Idepede Sample -Te Compare oe ample o a populaio Compare wo ample Differece bewee Mea i a Two-ample Experime

More information

6.003 Homework #9 Solutions

6.003 Homework #9 Solutions 6.003 Homework #9 Soluions Problems. Fourier varieies a. Deermine he Fourier series coefficiens of he following signal, which is periodic in 0. x () 0 3 0 a 0 5 a k a k 0 πk j3 e 0 e j πk 0 jπk πk e 0

More information

The equation to any straight line can be expressed in the form:

The equation to any straight line can be expressed in the form: Sring Graphs Par 1 Answers 1 TI-Nspire Invesigaion Suden min Aims Deermine a series of equaions of sraigh lines o form a paern similar o ha formed by he cables on he Jerusalem Chords Bridge. Deermine he

More information

Linear Motion, Speed & Velocity

Linear Motion, Speed & Velocity Add Iporan Linear Moion, Speed & Velociy Page: 136 Linear Moion, Speed & Velociy NGSS Sandard: N/A MA Curriculu Fraework (2006): 1.1, 1.2 AP Phyic 1 Learning Objecive: 3.A.1.1, 3.A.1.3 Knowledge/Underanding

More information

Math 2214 Solution Test 1A Spring 2016

Math 2214 Solution Test 1A Spring 2016 Mah 14 Soluion Tes 1A Spring 016 sec Problem 1: Wha is he larges -inerval for which ( 4) = has a guaraneed + unique soluion for iniial value (-1) = 3 according o he Exisence Uniqueness Theorem? Soluion

More information

d = ½(v o + v f) t distance = ½ (initial velocity + final velocity) time

d = ½(v o + v f) t distance = ½ (initial velocity + final velocity) time BULLSEYE Lab Name: ANSWER KEY Dae: Pre-AP Physics Lab Projecile Moion Weigh = 1 DIRECTIONS: Follow he insrucions below, build he ramp, ake your measuremens, and use your measuremens o make he calculaions

More information

20. Applications of the Genetic-Drift Model

20. Applications of the Genetic-Drift Model 0. Applicaions of he Geneic-Drif Model 1) Deermining he probabiliy of forming any paricular combinaion of genoypes in he nex generaion: Example: If he parenal allele frequencies are p 0 = 0.35 and q 0

More information

Flow Networks. Ma/CS 6a. Class 14: Flow Exercises

Flow Networks. Ma/CS 6a. Class 14: Flow Exercises 0/0/206 Ma/CS 6a Cla 4: Flow Exercie Flow Nework A flow nework i a digraph G = V, E, ogeher wih a ource verex V, a ink verex V, and a capaciy funcion c: E N. Capaciy Source 7 a b c d e Sink 0/0/206 Flow

More information

APPM 2360 Homework Solutions, Due June 10

APPM 2360 Homework Solutions, Due June 10 2.2.2: Find general soluions for he equaion APPM 2360 Homework Soluions, Due June 10 Soluion: Finding he inegraing facor, dy + 2y = 3e µ) = e 2) = e 2 Muliplying he differenial equaion by he inegraing

More information

Introduction to Congestion Games

Introduction to Congestion Games Algorihmic Game Theory, Summer 2017 Inroducion o Congeion Game Lecure 1 (5 page) Inrucor: Thoma Keelheim In hi lecure, we ge o know congeion game, which will be our running example for many concep in game

More information

Advanced Organic Chemistry

Advanced Organic Chemistry Lalic, G. Chem 53A Chemisry 53A Advanced Organic Chemisry Lecure noes 1 Kineics: A racical Approach Simple Kineics Scenarios Fiing Experimenal Daa Using Kineics o Deermine he Mechanism Doughery, D. A.,

More information

Summary:Linear Motion

Summary:Linear Motion Summary:Linear Moion D Saionary objec V Consan velociy D Disance increase uniformly wih ime D = v. a Consan acceleraion V D V = a. D = ½ a 2 Velociy increases uniformly wih ime Disance increases rapidly

More information

HYPOTHESIS TESTING. four steps. 1. State the hypothesis. 2. Set the criterion for rejecting. 3. Compute the test statistics. 4. Interpret the results.

HYPOTHESIS TESTING. four steps. 1. State the hypothesis. 2. Set the criterion for rejecting. 3. Compute the test statistics. 4. Interpret the results. Inrodcion o Saisics in Psychology PSY Professor Greg Francis Lecre 23 Hypohesis esing for correlaions Is here a correlaion beween homework and exam grades? for seps. Sae he hypohesis. 2. Se he crierion

More information

Summer Term Albert-Ludwigs-Universität Freiburg Empirische Forschung und Okonometrie. Time Series Analysis

Summer Term Albert-Ludwigs-Universität Freiburg Empirische Forschung und Okonometrie. Time Series Analysis Summer Term 2009 Alber-Ludwigs-Universiä Freiburg Empirische Forschung und Okonomerie Time Series Analysis Classical Time Series Models Time Series Analysis Dr. Sevap Kesel 2 Componens Hourly earnings:

More information

AP Calculus BC Chapter 10 Part 1 AP Exam Problems

AP Calculus BC Chapter 10 Part 1 AP Exam Problems AP Calculus BC Chaper Par AP Eam Problems All problems are NO CALCULATOR unless oherwise indicaed Parameric Curves and Derivaives In he y plane, he graph of he parameric equaions = 5 + and y= for, is a

More information

1 Motivation and Basic Definitions

1 Motivation and Basic Definitions CSCE : Deign and Analyi of Algorihm Noe on Max Flow Fall 20 (Baed on he preenaion in Chaper 26 of Inroducion o Algorihm, 3rd Ed. by Cormen, Leieron, Rive and Sein.) Moivaion and Baic Definiion Conider

More information

How to Solve System Dynamic s Problems

How to Solve System Dynamic s Problems How o Solve Sye Dynaic Proble A ye dynaic proble involve wo or ore bodie (objec) under he influence of everal exernal force. The objec ay uliaely re, ove wih conan velociy, conan acceleraion or oe cobinaion

More information

Physics 235 Chapter 2. Chapter 2 Newtonian Mechanics Single Particle

Physics 235 Chapter 2. Chapter 2 Newtonian Mechanics Single Particle Chaper 2 Newonian Mechanics Single Paricle In his Chaper we will review wha Newon s laws of mechanics ell us abou he moion of a single paricle. Newon s laws are only valid in suiable reference frames,

More information

Exercises, Part IV: THE LONG RUN

Exercises, Part IV: THE LONG RUN Exercie, Par IV: THE LOG RU 4. The olow Growh Model onider he olow rowh model wihou echnoloy prore and wih conan populaion. a) Define he eady ae condiion and repreen i raphically. b) how he effec of chane

More information

EECE 301 Signals & Systems Prof. Mark Fowler

EECE 301 Signals & Systems Prof. Mark Fowler EECE 31 Signal & Syem Prof. Mark Fowler Noe Se #27 C-T Syem: Laplace Tranform Power Tool for yem analyi Reading Aignmen: Secion 6.1 6.3 of Kamen and Heck 1/18 Coure Flow Diagram The arrow here how concepual

More information

Macroeconomics 1. Ali Shourideh. Final Exam

Macroeconomics 1. Ali Shourideh. Final Exam 4780 - Macroeconomic 1 Ali Shourideh Final Exam Problem 1. A Model of On-he-Job Search Conider he following verion of he McCall earch model ha allow for on-he-job-earch. In paricular, uppoe ha ime i coninuou

More information

Testing What You Know Now

Testing What You Know Now Tesing Wha You Know Now To bes each you, I need o know wha you know now Today we ake a well-esablished quiz ha is designed o ell me his To encourage you o ake he survey seriously, i will coun as a clicker

More information

Time series Decomposition method

Time series Decomposition method Time series Decomposiion mehod A ime series is described using a mulifacor model such as = f (rend, cyclical, seasonal, error) = f (T, C, S, e) Long- Iner-mediaed Seasonal Irregular erm erm effec, effec,

More information

Chapter 7: Inverse-Response Systems

Chapter 7: Inverse-Response Systems Chaper 7: Invere-Repone Syem Normal Syem Invere-Repone Syem Baic Sar ou in he wrong direcion End up in he original eady-ae gain value Two or more yem wih differen magniude and cale in parallel Main yem

More information

4 Sequences of measurable functions

4 Sequences of measurable functions 4 Sequences of measurable funcions 1. Le (Ω, A, µ) be a measure space (complee, afer a possible applicaion of he compleion heorem). In his chaper we invesigae relaions beween various (nonequivalen) convergences

More information

- Graphing: Position Velocity. Acceleration

- Graphing: Position Velocity. Acceleration Tes Wednesday, Jan 31 in 101 Clark Hall a 7PM Main Ideas in Class Today - Graphing: Posiion Velociy v avg = x f f x i i a avg = v f f v i i Acceleraion Pracice ess & key online. Tes over maerial up o secion

More information

Exponential Weighted Moving Average (EWMA) Chart Under The Assumption of Moderateness And Its 3 Control Limits

Exponential Weighted Moving Average (EWMA) Chart Under The Assumption of Moderateness And Its 3 Control Limits DOI: 0.545/mjis.07.5009 Exponenial Weighed Moving Average (EWMA) Char Under The Assumpion of Moderaeness And Is 3 Conrol Limis KALPESH S TAILOR Assisan Professor, Deparmen of Saisics, M. K. Bhavnagar Universiy,

More information

Laplace transfom: t-translation rule , Haynes Miller and Jeremy Orloff

Laplace transfom: t-translation rule , Haynes Miller and Jeremy Orloff Laplace ransfom: -ranslaion rule 8.03, Haynes Miller and Jeremy Orloff Inroducory example Consider he sysem ẋ + 3x = f(, where f is he inpu and x he response. We know is uni impulse response is 0 for

More information

RC, RL and RLC circuits

RC, RL and RLC circuits Name Dae Time o Complee h m Parner Course/ Secion / Grade RC, RL and RLC circuis Inroducion In his experimen we will invesigae he behavior of circuis conaining combinaions of resisors, capaciors, and inducors.

More information

Motion In One Dimension. Graphing Constant Speed

Motion In One Dimension. Graphing Constant Speed Moion In One Dimenion PLATO AND ARISTOTLE GALILEO GALILEI LEANING TOWER OF PISA Graphing Conan Speed Diance v. Time for Toy Car (0-5 ec.) be-fi line (from TI calculaor) d = 207.7 12.6 Diance (cm) 1000

More information

TSC 220X Spring 2011 Problem Set #5

TSC 220X Spring 2011 Problem Set #5 Name: TSC 220X Spring 2011 Problem Se #5 This problem se is due in class on Monday 21 March 2011. The problem se should be yped. We do no expec Pulizer Prize winning wriing, bu answers should be complee,

More information

Some Basic Information about M-S-D Systems

Some Basic Information about M-S-D Systems Some Basic Informaion abou M-S-D Sysems 1 Inroducion We wan o give some summary of he facs concerning unforced (homogeneous) and forced (non-homogeneous) models for linear oscillaors governed by second-order,

More information

UNC resolution Uncertainty Learning Objectives: measurement interval ( You will turn in two worksheets and

UNC resolution Uncertainty Learning Objectives: measurement interval ( You will turn in two worksheets and UNC Uncerainy revised Augus 30, 017 Learning Objecives: During his lab, you will learn how o 1. esimae he uncerainy in a direcly measured quaniy.. esimae he uncerainy in a quaniy ha is calculaed from quaniies

More information

Exponential and Logarithmic Functions -- ANSWERS -- Logarithms Practice Diploma ANSWERS 1

Exponential and Logarithmic Functions -- ANSWERS -- Logarithms Practice Diploma ANSWERS 1 Eponenial and Logarihmic Funcions -- ANSWERS -- Logarihms racice Diploma ANSWERS www.puremah.com Logarihms Diploma Syle racice Eam Answers. C. D 9. A 7. C. A. C. B 8. D. D. C NR. 8 9. C 4. C NR. NR 6.

More information

Methodology. -ratios are biased and that the appropriate critical values have to be increased by an amount. that depends on the sample size.

Methodology. -ratios are biased and that the appropriate critical values have to be increased by an amount. that depends on the sample size. Mehodology. Uni Roo Tess A ime series is inegraed when i has a mean revering propery and a finie variance. I is only emporarily ou of equilibrium and is called saionary in I(0). However a ime series ha

More information

Physics 20 Lesson 5 Graphical Analysis Acceleration

Physics 20 Lesson 5 Graphical Analysis Acceleration Physics 2 Lesson 5 Graphical Analysis Acceleraion I. Insananeous Velociy From our previous work wih consan speed and consan velociy, we know ha he slope of a posiion-ime graph is equal o he velociy of

More information

AP Chemistry--Chapter 12: Chemical Kinetics

AP Chemistry--Chapter 12: Chemical Kinetics AP Chemisry--Chaper 12: Chemical Kineics I. Reacion Raes A. The area of chemisry ha deals wih reacion raes, or how fas a reacion occurs, is called chemical kineics. B. The rae of reacion depends on he

More information

Licenciatura de ADE y Licenciatura conjunta Derecho y ADE. Hoja de ejercicios 2 PARTE A

Licenciatura de ADE y Licenciatura conjunta Derecho y ADE. Hoja de ejercicios 2 PARTE A Licenciaura de ADE y Licenciaura conjuna Derecho y ADE Hoja de ejercicios PARTE A 1. Consider he following models Δy = 0.8 + ε (1 + 0.8L) Δ 1 y = ε where ε and ε are independen whie noise processes. In

More information

Christos Papadimitriou & Luca Trevisan November 22, 2016

Christos Papadimitriou & Luca Trevisan November 22, 2016 U.C. Bereley CS170: Algorihms Handou LN-11-22 Chrisos Papadimiriou & Luca Trevisan November 22, 2016 Sreaming algorihms In his lecure and he nex one we sudy memory-efficien algorihms ha process a sream

More information

Phys1112: DC and RC circuits

Phys1112: DC and RC circuits Name: Group Members: Dae: TA s Name: Phys1112: DC and RC circuis Objecives: 1. To undersand curren and volage characerisics of a DC RC discharging circui. 2. To undersand he effec of he RC ime consan.

More information

Admin MAX FLOW APPLICATIONS. Flow graph/networks. Flow constraints 4/30/13. CS lunch today Grading. in-flow = out-flow for every vertex (except s, t)

Admin MAX FLOW APPLICATIONS. Flow graph/networks. Flow constraints 4/30/13. CS lunch today Grading. in-flow = out-flow for every vertex (except s, t) /0/ dmin lunch oday rading MX LOW PPLIION 0, pring avid Kauchak low graph/nework low nework direced, weighed graph (V, ) poiive edge weigh indicaing he capaciy (generally, aume ineger) conain a ingle ource

More information

Ground Rules. PC1221 Fundamentals of Physics I. Kinematics. Position. Lectures 3 and 4 Motion in One Dimension. A/Prof Tay Seng Chuan

Ground Rules. PC1221 Fundamentals of Physics I. Kinematics. Position. Lectures 3 and 4 Motion in One Dimension. A/Prof Tay Seng Chuan Ground Rules PC11 Fundamenals of Physics I Lecures 3 and 4 Moion in One Dimension A/Prof Tay Seng Chuan 1 Swich off your handphone and pager Swich off your lapop compuer and keep i No alking while lecure

More information

CHEMICAL KINETICS: 1. Rate Order Rate law Rate constant Half-life Temperature Dependence

CHEMICAL KINETICS: 1. Rate Order Rate law Rate constant Half-life Temperature Dependence CHEMICL KINETICS: Rae Order Rae law Rae consan Half-life Temperaure Dependence Chemical Reacions Kineics Chemical ineics is he sudy of ime dependence of he change in he concenraion of reacans and producs.

More information

SPH3U1 Lesson 03 Kinematics

SPH3U1 Lesson 03 Kinematics SPH3U1 Lesson 03 Kinemaics GRAPHICAL ANALYSIS LEARNING GOALS Sudens will Learn how o read values, find slopes and calculae areas on graphs. Learn wha hese values mean on boh posiion-ime and velociy-ime

More information