Solution to Test #1.

Size: px
Start display at page:

Download "Solution to Test #1."

Transcription

1 Solution to Tet #. Problem #. Lited below are recorded peed (in mile per hour, mph) of randomly elected car on a ection of Freeway 5 in Lo Angele. Data are orted A. Finih the following table: Cla Relative Frequency Cumulative Frequency Data Frequency (give anwer a reduced fraction) = = = SUM N/A (Do not Fill thi pace)

2 B. What number appear mot frequently? What i the frequency? 73 appear mot frequently, it frequency i 6 C. What i the mode? 73 D. What i the range? max min E. What i the midrange? max min midrange F. What i the median? There are two number in the middle, the 0 th, 68 and the t, 70, o the median i You might need thee number: The um of all the number: x 735 The um of quare: x x x 375 G. What i the mean peed x for thi random ample? (Round to the nearet thouandth.) 735 x x n H. What i the variance of the random ample? (Round to the nearet thouandth.) n x n n x x n x I. What i the tandard deviation of the random ample? (Round to the nearet thouandth.) n x n n x J. What i the percentile of data value 70? (Round to the nearet whole number.) There are 0 number that are maller than 70, o 0 The percentile of data value K. Find the firt Quartile, that i the 5 th percentile number in the data et, P5 Q.

3 L The tenth number i 65 and the eleventh number i 65, o Q P 65 5 L. Find the third Quartile, that i the 75 th percentile number in the data et, Q3 P75. L The 30 th number i 73 and the 3 t number i 73, o Q P 73 M. Find P 0 0 L The 4 th number i 59 and the 6 th number i 60, o P N. Find P 90 L The 36 th number i 75 and the 37 th number i 75, o P O. Find the inter-quartile range IQR? (definition in text book page 0): IQR Q3 Q P. Find the emi-inter-quartile range, (definition in text book page 0): Q Q Q. Find the mid-quartile: (definition in text book page 0): Q Q R percentile range: (definition in text book page 0): P 90 P S. Contruct a Box Plot for thi et of data. (Refer to example in the text.)

4 T. The word uual i for any data that fall within two tandard deviation of the mean, what i the uual range of peed? Give the range a an interval. x x , The uual range of peed i U. According to Chebyhev Theorem, at leat what percentage i expected to be in the uual range you jut calculated? At leat 75% i expected to be in the uual range of , V. Extra credit quetion: What i the actual percentage of number from thi data et that fall within the uual range that you jut calculated? There are actually 39 number fall within tandard deviation of the mean, here i the percentage: % Problem. The following table repreent a frequency ditribution of the duration time of (in econd) of eruption of the Old Faithful Geyer. From the information given in the table, fill out the 5 blank pace with the right number, repectively. Lower Cla Duration(Second) Upper Cla Cla Boundarie Lower Limit Upper Limit Boundarie Frequencie Midpoint >> Problem 3. If the Empirical Rule applie for all the IQ core, and we know the population mean and tandard deviation for all the IQ are 00, 5, repectively. Anwer the following quetion: a. What percentage of IQ core i between 85 and 5?

5 That i within one tandard deviation of the mean. According to Empirical Rule, 68% of the IQ are between 85 and 5. b. What i the uual range of IQ core? Give your anwer a an interval. The uual range i defined a within two tandard deviation of the mean, which i x x The uual range of IQ core i 70,30 c. Thoma Edion IQ core i 45, but that matter only % of the time becaue geniu i % inpiration and 99% perpiration. I hi IQ conidered uual or unuual if ABOVE three tandard deviation of the mean IQ core i conidered Geniu? Explain. x IQ core ABOVE 45 i then conidered Geniu. Since Thoma Edion IQ core i 45 which i NOT ABOVE the 45 threhold. Thu, he i not a geniu according to thi criterion. However, geniu i % inpiration and 99% perpiration, according to him. Problem 4. Lited below are the budget (in million of dollar) and the gro receipt (in million of dollar) for randomly elected movie. Budget x Gro Receipt y x SUM x 68 y 00 x 750 y xy y xy a. Compute the correlation coefficient r. Round the anwer to the nearet thouandth (three decimal place).

6 r n n xy x y x x n y y b. Find the critical value from the table in Page 760 for the ignificance level of α = Critical Value are ±0.754 for n = 7, α = 0.05 c. Could we conidered the two variable, x and y linearly correlated? Why? We could conider that the two variable are linearly correlated ince r i bigger than and i maller than. d. Extra credit, find the linear regreion line uing the formula in ection 0.3 for the lope and y-intercept. b n xy x y n x x b 0 y x x xy n x ŷ = x OR: Ue thee formula: y b0 y rx x b r

7 00 y 68 y x x n 7 n 7 y x b y r x b 0 y rx e. Extra credit, make a prediction according to the linear regreion line you have found in c. for a budget of 00 million, what might be the value etimated for the Gro Receipt? Show all your calculation. Problem 5. (0 point) With a height of 75 inche, Lyndon Johnon wa the tallet preident of the pat century. With a height of 67 inche, William McKinley wa the hortet preident of the pat century. With a height of 85 inche, Shaquille O Neal i the tallet player on the Miami Heat Baketball team. The mean height of the Preident in the pat century i 7.5 inche and a tandard deviation. inche. The mean height of the Baketball player for the Miami Heat i 80 inche and a tandard deviation 3.3 inche. a. What i the z-core of Lyndon Johnon? x Lyndon Johnon:. 67. b. What i the z-core of William McKinley if the mean height of the Preident in the pat century i 7.5 inche and a tandard deviation. inche? x William McKinley:. 4. c. What i the z-core of Shaquille O Neal? x Shaquille O Neal d. Who i relatively taller: Lyndon Johnon among the preident of the pat century, or Shaquille O Neal among the player on hi Miami Heat Team? Explain.

8 Lyndon Johnon height i.67 tandard deviation above the mean, and Shaquille O Neal height i.5 deviation above the mean. Lyndon Johnon height among preident of the pat century i relatively greater than Shaquille O Neal height among the Miami Heat baketball player. Shaquille O Neal i much taller than Lyndon Johnon, but Johnon i relatively taller than when compared to hi colleague.

Social Studies 201 Notes for November 14, 2003

Social Studies 201 Notes for November 14, 2003 1 Social Studie 201 Note for November 14, 2003 Etimation of a mean, mall ample ize Section 8.4, p. 501. When a reearcher ha only a mall ample ize available, the central limit theorem doe not apply to the

More information

μ + = σ = D 4 σ = D 3 σ = σ = All units in parts (a) and (b) are in V. (1) x chart: Center = μ = 0.75 UCL =

μ + = σ = D 4 σ = D 3 σ = σ = All units in parts (a) and (b) are in V. (1) x chart: Center = μ = 0.75 UCL = Our online Tutor are available 4*7 to provide Help with Proce control ytem Homework/Aignment or a long term Graduate/Undergraduate Proce control ytem Project. Our Tutor being experienced and proficient

More information

Suggestions - Problem Set (a) Show the discriminant condition (1) takes the form. ln ln, # # R R

Suggestions - Problem Set (a) Show the discriminant condition (1) takes the form. ln ln, # # R R Suggetion - Problem Set 3 4.2 (a) Show the dicriminant condition (1) take the form x D Ð.. Ñ. D.. D. ln ln, a deired. We then replace the quantitie. 3ß D3 by their etimate to get the proper form for thi

More information

Regression. What is regression? Linear Regression. Cal State Northridge Ψ320 Andrew Ainsworth PhD

Regression. What is regression? Linear Regression. Cal State Northridge Ψ320 Andrew Ainsworth PhD Regreion Cal State Northridge Ψ30 Andrew Ainworth PhD What i regreion? How do we predict one variable from another? How doe one variable change a the other change? Caue and effect Linear Regreion A technique

More information

Social Studies 201 Notes for March 18, 2005

Social Studies 201 Notes for March 18, 2005 1 Social Studie 201 Note for March 18, 2005 Etimation of a mean, mall ample ize Section 8.4, p. 501. When a reearcher ha only a mall ample ize available, the central limit theorem doe not apply to the

More information

Comparing Means: t-tests for Two Independent Samples

Comparing Means: t-tests for Two Independent Samples Comparing ean: t-tet for Two Independent Sample Independent-eaure Deign t-tet for Two Independent Sample Allow reearcher to evaluate the mean difference between two population uing data from two eparate

More information

Source slideplayer.com/fundamentals of Analytical Chemistry, F.J. Holler, S.R.Crouch. Chapter 6: Random Errors in Chemical Analysis

Source slideplayer.com/fundamentals of Analytical Chemistry, F.J. Holler, S.R.Crouch. Chapter 6: Random Errors in Chemical Analysis Source lideplayer.com/fundamental of Analytical Chemitry, F.J. Holler, S.R.Crouch Chapter 6: Random Error in Chemical Analyi Random error are preent in every meaurement no matter how careful the experimenter.

More information

Chapter 12 Simple Linear Regression

Chapter 12 Simple Linear Regression Chapter 1 Simple Linear Regreion Introduction Exam Score v. Hour Studied Scenario Regreion Analyi ued to quantify the relation between (or more) variable o you can predict the value of one variable baed

More information

Suggested Answers To Exercises. estimates variability in a sampling distribution of random means. About 68% of means fall

Suggested Answers To Exercises. estimates variability in a sampling distribution of random means. About 68% of means fall Beyond Significance Teting ( nd Edition), Rex B. Kline Suggeted Anwer To Exercie Chapter. The tatitic meaure variability among core at the cae level. In a normal ditribution, about 68% of the core fall

More information

SIMPLE LINEAR REGRESSION

SIMPLE LINEAR REGRESSION SIMPLE LINEAR REGRESSION In linear regreion, we conider the frequency ditribution of one variable (Y) at each of everal level of a econd variable (). Y i known a the dependent variable. The variable for

More information

Range The range is the simplest of the three measures and is defined now.

Range The range is the simplest of the three measures and is defined now. Measures of Variation EXAMPLE A testing lab wishes to test two experimental brands of outdoor paint to see how long each will last before fading. The testing lab makes 6 gallons of each paint to test.

More information

CHAPTER 6. Estimation

CHAPTER 6. Estimation CHAPTER 6 Etimation Definition. Statitical inference i the procedure by which we reach a concluion about a population on the bai of information contained in a ample drawn from that population. Definition.

More information

PhysicsAndMathsTutor.com

PhysicsAndMathsTutor.com 1. A teacher wihe to tet whether playing background muic enable tudent to complete a tak more quickly. The ame tak wa completed by 15 tudent, divided at random into two group. The firt group had background

More information

Lecture 7: Testing Distributions

Lecture 7: Testing Distributions CSE 5: Sublinear (and Streaming) Algorithm Spring 014 Lecture 7: Teting Ditribution April 1, 014 Lecturer: Paul Beame Scribe: Paul Beame 1 Teting Uniformity of Ditribution We return today to property teting

More information

Cumulative Review of Calculus

Cumulative Review of Calculus Cumulative Review of Calculu. Uing the limit definition of the lope of a tangent, determine the lope of the tangent to each curve at the given point. a. f 5,, 5 f,, f, f 5,,,. The poition, in metre, of

More information

Fair Game Review. Chapter 6 A B C D E Complete the number sentence with <, >, or =

Fair Game Review. Chapter 6 A B C D E Complete the number sentence with <, >, or = Name Date Chapter 6 Fair Game Review Complete the number entence with , or =. 1..4.45. 6.01 6.1..50.5 4. 0.84 0.91 Find three decimal that make the number entence true. 5. 5. 6..65 > 7..18 8. 0.0

More information

Linear Motion, Speed & Velocity

Linear Motion, Speed & Velocity Add Important Linear Motion, Speed & Velocity Page: 136 Linear Motion, Speed & Velocity NGSS Standard: N/A MA Curriculum Framework (006): 1.1, 1. AP Phyic 1 Learning Objective: 3.A.1.1, 3.A.1.3 Knowledge/Undertanding

More information

1. The F-test for Equality of Two Variances

1. The F-test for Equality of Two Variances . The F-tet for Equality of Two Variance Previouly we've learned how to tet whether two population mean are equal, uing data from two independent ample. We can alo tet whether two population variance are

More information

(b) 99%; n = 15; σ is unknown; population appears to be normally distributed.

(b) 99%; n = 15; σ is unknown; population appears to be normally distributed. MTH 345 Exam 3 Fall 2013 Jutify all anwer with neat and organized work. Clearly indicate your anwer. 100 point poible. 1. (12 pt.) Women height are normally ditributed with mean 63.6 in. and tandard deviation

More information

CEE 320 Midterm Examination (1 hour)

CEE 320 Midterm Examination (1 hour) Examination (1 hour) Pleae write your name on thi cover. Pleae write you lat name on all other exam page Thi examination i open-book, open-note. There are 5 quetion worth a total of 100 point. Each quetion

More information

Week 3 Statistics for bioinformatics and escience

Week 3 Statistics for bioinformatics and escience Week 3 Statitic for bioinformatic and escience Line Skotte 28. november 2008 2.9.3-4) In thi eercie we conider microrna data from Human and Moue. The data et repreent 685 independent realiation of the

More information

MINITAB Stat Lab 3

MINITAB Stat Lab 3 MINITAB Stat 20080 Lab 3. Statitical Inference In the previou lab we explained how to make prediction from a imple linear regreion model and alo examined the relationhip between the repone and predictor

More information

Fair Game Review. Chapter 7 A B C D E Name Date. Complete the number sentence with <, >, or =

Fair Game Review. Chapter 7 A B C D E Name Date. Complete the number sentence with <, >, or = Name Date Chapter 7 Fair Game Review Complete the number entence with , or =. 1. 3.4 3.45 2. 6.01 6.1 3. 3.50 3.5 4. 0.84 0.91 Find three decimal that make the number entence true. 5. 5.2 6. 2.65 >

More information

SAT Math Notes. By Steve Baba, Ph.D FREE for individual or classroom use. Not free for commercial or online use.

SAT Math Notes. By Steve Baba, Ph.D FREE for individual or classroom use. Not free for commercial or online use. SAT Math Note B Steve Baba, Ph.D. 2008. FREE for individual or claroom ue. Not free for commercial or online ue. For SAT reading ee m ite: www.freevocabular.com for a free lit of 5000 SAT word with brief

More information

Standard normal distribution. t-distribution, (df=5) t-distribution, (df=2) PDF created with pdffactory Pro trial version

Standard normal distribution. t-distribution, (df=5) t-distribution, (df=2) PDF created with pdffactory Pro trial version t-ditribution In biological reearch the population variance i uually unknown and an unbiaed etimate,, obtained from the ample data, ha to be ued in place of σ. The propertie of t- ditribution are: -It

More information

e st t u(t 2) dt = lim t dt = T 2 2 e st = T e st lim + e st

e st t u(t 2) dt = lim t dt = T 2 2 e st = T e st lim + e st Math 46, Profeor David Levermore Anwer to Quetion for Dicuion Friday, 7 October 7 Firt Set of Quetion ( Ue the definition of the Laplace tranform to compute Lf]( for the function f(t = u(t t, where u i

More information

ON THE APPROXIMATION ERROR IN HIGH DIMENSIONAL MODEL REPRESENTATION. Xiaoqun Wang

ON THE APPROXIMATION ERROR IN HIGH DIMENSIONAL MODEL REPRESENTATION. Xiaoqun Wang Proceeding of the 2008 Winter Simulation Conference S. J. Maon, R. R. Hill, L. Mönch, O. Roe, T. Jefferon, J. W. Fowler ed. ON THE APPROXIMATION ERROR IN HIGH DIMENSIONAL MODEL REPRESENTATION Xiaoqun Wang

More information

Z a>2 s 1n = X L - m. X L = m + Z a>2 s 1n X L = The decision rule for this one-tail test is

Z a>2 s 1n = X L - m. X L = m + Z a>2 s 1n X L = The decision rule for this one-tail test is M09_BERE8380_12_OM_C09.QD 2/21/11 3:44 PM Page 1 9.6 The Power of a Tet 9.6 The Power of a Tet 1 Section 9.1 defined Type I and Type II error and their aociated rik. Recall that a repreent the probability

More information

Math Skills. Scientific Notation. Uncertainty in Measurements. Appendix A5 SKILLS HANDBOOK

Math Skills. Scientific Notation. Uncertainty in Measurements. Appendix A5 SKILLS HANDBOOK ppendix 5 Scientific Notation It i difficult to work with very large or very mall number when they are written in common decimal notation. Uually it i poible to accommodate uch number by changing the SI

More information

Problem Set 8 Solutions

Problem Set 8 Solutions Deign and Analyi of Algorithm April 29, 2015 Maachuett Intitute of Technology 6.046J/18.410J Prof. Erik Demaine, Srini Devada, and Nancy Lynch Problem Set 8 Solution Problem Set 8 Solution Thi problem

More information

Understand how units behave and combine algebraically. Know the 4 common prefixes and their numeric meanings.

Understand how units behave and combine algebraically. Know the 4 common prefixes and their numeric meanings. Add Important The Metric Sytem Page: 91 NGSS Standard: N/A The Metric Sytem MA Curriculum Framework (006): N/A AP Phyic 1 Learning Objective: N/A Knowledge/Undertanding: Skill: Undertand how unit behave

More information

Chapter 2 Sampling and Quantization. In order to investigate sampling and quantization, the difference between analog

Chapter 2 Sampling and Quantization. In order to investigate sampling and quantization, the difference between analog Chapter Sampling and Quantization.1 Analog and Digital Signal In order to invetigate ampling and quantization, the difference between analog and digital ignal mut be undertood. Analog ignal conit of continuou

More information

If Y is normally Distributed, then and 2 Y Y 10. σ σ

If Y is normally Distributed, then and 2 Y Y 10. σ σ ull Hypothei Significance Teting V. APS 50 Lecture ote. B. Dudek. ot for General Ditribution. Cla Member Uage Only. Chi-Square and F-Ditribution, and Diperion Tet Recall from Chapter 4 material on: ( )

More information

Lecture 9: Shor s Algorithm

Lecture 9: Shor s Algorithm Quantum Computation (CMU 8-859BB, Fall 05) Lecture 9: Shor Algorithm October 7, 05 Lecturer: Ryan O Donnell Scribe: Sidhanth Mohanty Overview Let u recall the period finding problem that wa et up a a function

More information

Midterm Review - Part 1

Midterm Review - Part 1 Honor Phyic Fall, 2016 Midterm Review - Part 1 Name: Mr. Leonard Intruction: Complete the following workheet. SHOW ALL OF YOUR WORK. 1. Determine whether each tatement i True or Fale. If the tatement i

More information

Lecture 8: Period Finding: Simon s Problem over Z N

Lecture 8: Period Finding: Simon s Problem over Z N Quantum Computation (CMU 8-859BB, Fall 205) Lecture 8: Period Finding: Simon Problem over Z October 5, 205 Lecturer: John Wright Scribe: icola Rech Problem A mentioned previouly, period finding i a rephraing

More information

KEY. D. 1.3 kg m. Solution: Using conservation of energy on the swing, mg( h) = 1 2 mv2 v = 2mg( h)

KEY. D. 1.3 kg m. Solution: Using conservation of energy on the swing, mg( h) = 1 2 mv2 v = 2mg( h) Phy 5 - Fall 206 Extra credit review eion - Verion A KEY Thi i an extra credit review eion. t will be worth 30 point of extra credit. Dicu and work on the problem with your group. You may ue your text

More information

Multipurpose Small Area Estimation

Multipurpose Small Area Estimation Multipurpoe Small Area Etimation Hukum Chandra Univerity of Southampton, U.K. Ray Chamber Univerity of Wollongong, Autralia Weighting and Small Area Etimation Sample urvey are generally multivariate, in

More information

Physics 218: Exam 1. Class of 2:20pm. February 14th, You have the full class period to complete the exam.

Physics 218: Exam 1. Class of 2:20pm. February 14th, You have the full class period to complete the exam. Phyic 218: Exam 1 Cla of 2:20pm February 14th, 2012. Rule of the exam: 1. You have the full cla period to complete the exam. 2. Formulae are provided on the lat page. You may NOT ue any other formula heet.

More information

MATEMATIK Datum: Tid: eftermiddag. A.Heintz Telefonvakt: Anders Martinsson Tel.:

MATEMATIK Datum: Tid: eftermiddag. A.Heintz Telefonvakt: Anders Martinsson Tel.: MATEMATIK Datum: 20-08-25 Tid: eftermiddag GU, Chalmer Hjälpmedel: inga A.Heintz Telefonvakt: Ander Martinon Tel.: 073-07926. Löningar till tenta i ODE och matematik modellering, MMG5, MVE6. Define what

More information

White Rose Research Online URL for this paper: Version: Accepted Version

White Rose Research Online URL for this paper:   Version: Accepted Version Thi i a repoitory copy of Identification of nonlinear ytem with non-peritent excitation uing an iterative forward orthogonal leat quare regreion algorithm. White Roe Reearch Online URL for thi paper: http://eprint.whiteroe.ac.uk/107314/

More information

PROBABILITY AND STATISTICS. Least Squares Regression

PROBABILITY AND STATISTICS. Least Squares Regression PROBABILITY AND STATISTICS Leat Square Regreion LEAST-SQUARES REGRESSION What doe correlation give u? If a catterplot how a linear relationhip one wa to ummarize the overall pattern of the catterplot i

More information

represented in the table? How are they shown on the graph?

represented in the table? How are they shown on the graph? Application. The El Pao Middle School girl baketball team i going from El Pao to San Antonio for the Tea tate championhip game. The trip will be 0 mile. Their bu travel at an average peed of 0 mile per

More information

After the invention of the steam engine in the late 1700s by the Scottish engineer

After the invention of the steam engine in the late 1700s by the Scottish engineer Introduction to Statitic 22 After the invention of the team engine in the late 1700 by the Scottih engineer Jame Watt, the production of machine-made good became widepread during the 1800. However, it

More information

The machines in the exercise work as follows:

The machines in the exercise work as follows: Tik-79.148 Spring 2001 Introduction to Theoretical Computer Science Tutorial 9 Solution to Demontration Exercie 4. Contructing a complex Turing machine can be very laboriou. With the help of machine chema

More information

IEOR 3106: Fall 2013, Professor Whitt Topics for Discussion: Tuesday, November 19 Alternating Renewal Processes and The Renewal Equation

IEOR 3106: Fall 2013, Professor Whitt Topics for Discussion: Tuesday, November 19 Alternating Renewal Processes and The Renewal Equation IEOR 316: Fall 213, Profeor Whitt Topic for Dicuion: Tueday, November 19 Alternating Renewal Procee and The Renewal Equation 1 Alternating Renewal Procee An alternating renewal proce alternate between

More information

Confidence Intervals and Hypothesis Testing of a Population Mean (Variance Known)

Confidence Intervals and Hypothesis Testing of a Population Mean (Variance Known) Confidence Interval and Hypothei Teting of a Population Mean (Variance Known) Confidence Interval One-ided confidence level for lower bound, X l = X Z α One ided confidence interval for upper bound, X

More information

(3) A bilinear map B : S(R n ) S(R m ) B is continuous (for the product topology) if and only if there exist C, N and M such that

(3) A bilinear map B : S(R n ) S(R m ) B is continuous (for the product topology) if and only if there exist C, N and M such that The material here can be found in Hörmander Volume 1, Chapter VII but he ha already done almot all of ditribution theory by thi point(!) Johi and Friedlander Chapter 8. Recall that S( ) i a complete metric

More information

Chapter 3 Data Description

Chapter 3 Data Description Chapter 3 Data Description Section 3.1: Measures of Central Tendency Section 3.2: Measures of Variation Section 3.3: Measures of Position Section 3.1: Measures of Central Tendency Definition of Average

More information

March 18, 2014 Academic Year 2013/14

March 18, 2014 Academic Year 2013/14 POLITONG - SHANGHAI BASIC AUTOMATIC CONTROL Exam grade March 8, 4 Academic Year 3/4 NAME (Pinyin/Italian)... STUDENT ID Ue only thee page (including the back) for anwer. Do not ue additional heet. Ue of

More information

Unit 2. Describing Data: Numerical

Unit 2. Describing Data: Numerical Unit 2 Describing Data: Numerical Describing Data Numerically Describing Data Numerically Central Tendency Arithmetic Mean Median Mode Variation Range Interquartile Range Variance Standard Deviation Coefficient

More information

a = f s,max /m = s g. 4. We first analyze the forces on the pig of mass m. The incline angle is.

a = f s,max /m = s g. 4. We first analyze the forces on the pig of mass m. The incline angle is. Chapter 6 1. The greatet deceleration (of magnitude a) i provided by the maximum friction force (Eq. 6-1, with = mg in thi cae). Uing ewton econd law, we find a = f,max /m = g. Eq. -16 then give the hortet

More information

Fair Game Review. Chapter 6. Evaluate the expression. 3. ( ) 7. Find ± Find Find Find the side length s of the square.

Fair Game Review. Chapter 6. Evaluate the expression. 3. ( ) 7. Find ± Find Find Find the side length s of the square. Name Date Chapter 6 Evaluate the epreion. Fair Game Review 1. 5 1 6 3 + 8. 18 9 + 0 5 3 3 1 + +. 9 + 7( 8) + 5 0 + ( 6 8) 1 3 3 3. ( ) 5. Find 81. 6. Find 5. 7. Find ± 16. 8. Find the ide length of the

More information

NEGATIVE z Scores. TABLE A-2 Standard Normal (z) Distribution: Cumulative Area from the LEFT. (continued)

NEGATIVE z Scores. TABLE A-2 Standard Normal (z) Distribution: Cumulative Area from the LEFT. (continued) NEGATIVE z Score z 0 TALE A- Standard Normal (z) Ditribution: Cumulative Area from the LEFT z.00.01.0.03.04.05.06.07.08.09-3.50 and lower.0001-3.4.0003.0003.0003.0003.0003.0003.0003.0003.0003.000-3.3.0005.0005.0005.0004.0004.0004.0004.0004.0004.0003-3..0007.0007.0006.0006.0006.0006.0006.0005.0005.0005-3.1.0010.0009.0009.0009.0008.0008.0008.0008.0007.0007-3.0.0013.0013.0013.001.001.0011.0011.0011.0010.0010

More information

L Exercise , page Exercise , page 523.

L Exercise , page Exercise , page 523. Homework #7* Statitic 1 L Eercie 12.2.2, pae 522. 2. Eercie 12.2.6, pae 523. 3. Eercie 12.2.7, pae 523. 4. Eercie 12.3.4, pae 535. 5. Eercie 12.3.5, pae 535. 6. Eercie12.4.3, pae 543. 7. Eercie 12.4.4,

More information

(b) Is the game below solvable by iterated strict dominance? Does it have a unique Nash equilibrium?

(b) Is the game below solvable by iterated strict dominance? Does it have a unique Nash equilibrium? 14.1 Final Exam Anwer all quetion. You have 3 hour in which to complete the exam. 1. (60 Minute 40 Point) Anwer each of the following ubquetion briefly. Pleae how your calculation and provide rough explanation

More information

time? How will changes in vertical drop of the course affect race time? How will changes in the distance between turns affect race time?

time? How will changes in vertical drop of the course affect race time? How will changes in the distance between turns affect race time? Unit 1 Leon 1 Invetigation 1 Think About Thi Situation Name: Conider variou port that involve downhill racing. Think about the factor that decreae or increae the time it take to travel from top to bottom.

More information

Halliday/Resnick/Walker 7e Chapter 6

Halliday/Resnick/Walker 7e Chapter 6 HRW 7e Chapter 6 Page of Halliday/Renick/Walker 7e Chapter 6 3. We do not conider the poibility that the bureau might tip, and treat thi a a purely horizontal motion problem (with the peron puh F in the

More information

Math 273 Solutions to Review Problems for Exam 1

Math 273 Solutions to Review Problems for Exam 1 Math 7 Solution to Review Problem for Exam True or Fale? Circle ONE anwer for each Hint: For effective tudy, explain why if true and give a counterexample if fale (a) T or F : If a b and b c, then a c

More information

Objective A: Mean, Median and Mode Three measures of central of tendency: the mean, the median, and the mode.

Objective A: Mean, Median and Mode Three measures of central of tendency: the mean, the median, and the mode. Chapter 3 Numerically Summarizing Data Chapter 3.1 Measures of Central Tendency Objective A: Mean, Median and Mode Three measures of central of tendency: the mean, the median, and the mode. A1. Mean The

More information

Bogoliubov Transformation in Classical Mechanics

Bogoliubov Transformation in Classical Mechanics Bogoliubov Tranformation in Claical Mechanic Canonical Tranformation Suppoe we have a et of complex canonical variable, {a j }, and would like to conider another et of variable, {b }, b b ({a j }). How

More information

Constant Force: Projectile Motion

Constant Force: Projectile Motion Contant Force: Projectile Motion Abtract In thi lab, you will launch an object with a pecific initial velocity (magnitude and direction) and determine the angle at which the range i a maximum. Other tak,

More information

Preemptive scheduling on a small number of hierarchical machines

Preemptive scheduling on a small number of hierarchical machines Available online at www.ciencedirect.com Information and Computation 06 (008) 60 619 www.elevier.com/locate/ic Preemptive cheduling on a mall number of hierarchical machine György Dóa a, Leah Eptein b,

More information

Orbitals, Shapes and Polarity Quiz

Orbitals, Shapes and Polarity Quiz Orbital, Shae and Polarity Quiz Name: /17 Knowledge. Anwer the following quetion on foolca. /2 1. Exlain why the ub-level can aear to be herical like the ub-level? /2 2.a) What i the maximum number of

More information

Section 3. Measures of Variation

Section 3. Measures of Variation Section 3 Measures of Variation Range Range = (maximum value) (minimum value) It is very sensitive to extreme values; therefore not as useful as other measures of variation. Sample Standard Deviation The

More information

Chapter. Numerically Summarizing Data Pearson Prentice Hall. All rights reserved

Chapter. Numerically Summarizing Data Pearson Prentice Hall. All rights reserved Chapter 3 Numerically Summarizing Data Section 3.1 Measures of Central Tendency Objectives 1. Determine the arithmetic mean of a variable from raw data 2. Determine the median of a variable from raw data

More information

Unit I Review Worksheet Key

Unit I Review Worksheet Key Unit I Review Workheet Key 1. Which of the following tatement about vector and calar are TRUE? Anwer: CD a. Fale - Thi would never be the cae. Vector imply are direction-conciou, path-independent quantitie

More information

CHAPTER 8 OBSERVER BASED REDUCED ORDER CONTROLLER DESIGN FOR LARGE SCALE LINEAR DISCRETE-TIME CONTROL SYSTEMS

CHAPTER 8 OBSERVER BASED REDUCED ORDER CONTROLLER DESIGN FOR LARGE SCALE LINEAR DISCRETE-TIME CONTROL SYSTEMS CHAPTER 8 OBSERVER BASED REDUCED ORDER CONTROLLER DESIGN FOR LARGE SCALE LINEAR DISCRETE-TIME CONTROL SYSTEMS 8.1 INTRODUCTION 8.2 REDUCED ORDER MODEL DESIGN FOR LINEAR DISCRETE-TIME CONTROL SYSTEMS 8.3

More information

Physics 2212 G Quiz #2 Solutions Spring 2018

Physics 2212 G Quiz #2 Solutions Spring 2018 Phyic 2212 G Quiz #2 Solution Spring 2018 I. (16 point) A hollow inulating phere ha uniform volume charge denity ρ, inner radiu R, and outer radiu 3R. Find the magnitude of the electric field at a ditance

More information

Lecture 11. Data Description Estimation

Lecture 11. Data Description Estimation Lecture 11 Data Description Estimation Measures of Central Tendency (continued, see last lecture) Sample mean, population mean Sample mean for frequency distributions The median The mode The midrange 3-22

More information

Properties of Z-transform Transform 1 Linearity a

Properties of Z-transform Transform 1 Linearity a Midterm 3 (Fall 6 of EEG:. Thi midterm conit of eight ingle-ided page. The firt three page contain variou table followed by FOUR eam quetion and one etra workheet. You can tear out any page but make ure

More information

into a discrete time function. Recall that the table of Laplace/z-transforms is constructed by (i) selecting to get

into a discrete time function. Recall that the table of Laplace/z-transforms is constructed by (i) selecting to get Lecture 25 Introduction to Some Matlab c2d Code in Relation to Sampled Sytem here are many way to convert a continuou time function, { h( t) ; t [0, )} into a dicrete time function { h ( k) ; k {0,,, }}

More information

No-load And Blocked Rotor Test On An Induction Machine

No-load And Blocked Rotor Test On An Induction Machine No-load And Blocked Rotor Tet On An Induction Machine Aim To etimate magnetization and leakage impedance parameter of induction machine uing no-load and blocked rotor tet Theory An induction machine in

More information

Clustering Methods without Given Number of Clusters

Clustering Methods without Given Number of Clusters Clutering Method without Given Number of Cluter Peng Xu, Fei Liu Introduction A we now, mean method i a very effective algorithm of clutering. It mot powerful feature i the calability and implicity. However,

More information

NCAAPMT Calculus Challenge Challenge #3 Due: October 26, 2011

NCAAPMT Calculus Challenge Challenge #3 Due: October 26, 2011 NCAAPMT Calculu Challenge 011 01 Challenge #3 Due: October 6, 011 A Model of Traffic Flow Everyone ha at ome time been on a multi-lane highway and encountered road contruction that required the traffic

More information

Fundamentals of Astrodynamics and Applications 4 th Ed

Fundamentals of Astrodynamics and Applications 4 th Ed Fundamental of Atrodynamic and Application 4 th Ed Conolidated Errata February 4, 08 Thi liting i an on-going document of correction and clarification encountered in the book. I appreciate any comment

More information

SOLUTIONS TO ALGEBRAIC GEOMETRY AND ARITHMETIC CURVES BY QING LIU. I will collect my solutions to some of the exercises in this book in this document.

SOLUTIONS TO ALGEBRAIC GEOMETRY AND ARITHMETIC CURVES BY QING LIU. I will collect my solutions to some of the exercises in this book in this document. SOLUTIONS TO ALGEBRAIC GEOMETRY AND ARITHMETIC CURVES BY QING LIU CİHAN BAHRAN I will collect my olution to ome of the exercie in thi book in thi document. Section 2.1 1. Let A = k[[t ]] be the ring of

More information

Moment of Inertia of an Equilateral Triangle with Pivot at one Vertex

Moment of Inertia of an Equilateral Triangle with Pivot at one Vertex oment of nertia of an Equilateral Triangle with Pivot at one Vertex There are two wa (at leat) to derive the expreion f an equilateral triangle that i rotated about one vertex, and ll how ou both here.

More information

AP Chemistry: Kinetics Practice Problems

AP Chemistry: Kinetics Practice Problems AP Chemitr: Kinetic Practice Problem Direction: Write our anwer to the following quetion in the pace provided. For problem olving, how all of our work. Make ure that our anwer how proper unit, notation,

More information

Uniform Acceleration Problems Chapter 2: Linear Motion

Uniform Acceleration Problems Chapter 2: Linear Motion Name Date Period Uniform Acceleration Problem Chapter 2: Linear Motion INSTRUCTIONS: For thi homework, you will be drawing a coordinate axi (in math lingo: an x-y board ) to olve kinematic (motion) problem.

More information

EC381/MN308 Probability and Some Statistics. Lecture 7 - Outline. Chapter Cumulative Distribution Function (CDF) Continuous Random Variables

EC381/MN308 Probability and Some Statistics. Lecture 7 - Outline. Chapter Cumulative Distribution Function (CDF) Continuous Random Variables EC38/MN38 Probability and Some Statitic Yanni Pachalidi yannip@bu.edu, http://ionia.bu.edu/ Lecture 7 - Outline. Continuou Random Variable Dept. of Manufacturing Engineering Dept. of Electrical and Computer

More information

Optimal Coordination of Samples in Business Surveys

Optimal Coordination of Samples in Business Surveys Paper preented at the ICES-III, June 8-, 007, Montreal, Quebec, Canada Optimal Coordination of Sample in Buine Survey enka Mach, Ioana Şchiopu-Kratina, Philip T Rei, Jean-Marc Fillion Statitic Canada New

More information

Design spacecraft external surfaces to ensure 95 percent probability of no mission-critical failures from particle impact.

Design spacecraft external surfaces to ensure 95 percent probability of no mission-critical failures from particle impact. PREFERRED RELIABILITY PAGE 1 OF 6 PRACTICES METEOROIDS & SPACE DEBRIS Practice: Deign pacecraft external urface to enure 95 percent probability of no miion-critical failure from particle impact. Benefit:

More information

One Class of Splitting Iterative Schemes

One Class of Splitting Iterative Schemes One Cla of Splitting Iterative Scheme v Ciegi and V. Pakalnytė Vilniu Gedimina Technical Univerity Saulėtekio al. 11, 2054, Vilniu, Lithuania rc@fm.vtu.lt Abtract. Thi paper deal with the tability analyi

More information

Solving Radical Equations

Solving Radical Equations 10. Solving Radical Equation Eential Quetion How can you olve an equation that contain quare root? Analyzing a Free-Falling Object MODELING WITH MATHEMATICS To be proficient in math, you need to routinely

More information

A tutorial on conformal prediction

A tutorial on conformal prediction A tutorial on conformal prediction Glenn Shafer and Vladimir Vovk praktiqekie vyvody teorii vero tnote mogut bytь obonovany v kaqetve ledtvi gipotez o predelьno pri dannyh ograniqeni h loжnoti izuqaemyh

More information

List coloring hypergraphs

List coloring hypergraphs Lit coloring hypergraph Penny Haxell Jacque Vertraete Department of Combinatoric and Optimization Univerity of Waterloo Waterloo, Ontario, Canada pehaxell@uwaterloo.ca Department of Mathematic Univerity

More information

Inference for Two Stage Cluster Sampling: Equal SSU per PSU. Projections of SSU Random Variables on Each SSU selection.

Inference for Two Stage Cluster Sampling: Equal SSU per PSU. Projections of SSU Random Variables on Each SSU selection. Inference for Two Stage Cluter Sampling: Equal SSU per PSU Projection of SSU andom Variable on Eac SSU election By Ed Stanek Introduction We review etimating equation for PSU mean in a two tage cluter

More information

ONLINE APPENDIX FOR HOUSING BOOMS, MANUFACTURING DECLINE,

ONLINE APPENDIX FOR HOUSING BOOMS, MANUFACTURING DECLINE, ONLINE APPENDIX FOR HOUSING BOOS, ANUFACTURING DECLINE, AND LABOR ARKET OUTCOES Kerwin Kofi Charle Erik Hurt atthew J. Notowidigdo July 2017 A. Background on Propertie of Frechet Ditribution Thi ection

More information

skipping section 6.6 / 5.6 (generating permutations and combinations) concludes basic counting in Chapter 6 / 5

skipping section 6.6 / 5.6 (generating permutations and combinations) concludes basic counting in Chapter 6 / 5 kiing ection 6.6 / 5.6 generating ermutation and combination conclude baic counting in Chater 6 / 5 on to Chater 7 / 6: Dicrete robability before we go to trickier counting in Chater 8 / 7 age 431-475

More information

Bio 112 Lecture Notes; Scientific Method

Bio 112 Lecture Notes; Scientific Method Bio Lecture ote; Scientific Method What Scientit Do: Scientit collect data and develop theorie, model, and law about how nature work. Science earche for natural caue to eplain natural phenomenon Purpoe

More information

CE3502. EMMA COMPARISON OF NUMBERS. Earth Week: Film, lecture International World Water Day: Monday Posters on water research at MTU

CE3502. EMMA COMPARISON OF NUMBERS. Earth Week: Film, lecture International World Water Day: Monday Posters on water research at MTU CE350. EMMA COMPARISON OF NUMBERS Earth Week: Film, lecture International World Water Da: Monda Poter on water reearch at MTU Majora Carter Greening the Ghetto Saturda, March 0 7:30pm Roza Center Majora

More information

Solving Differential Equations by the Laplace Transform and by Numerical Methods

Solving Differential Equations by the Laplace Transform and by Numerical Methods 36CH_PHCalter_TechMath_95099 3//007 :8 PM Page Solving Differential Equation by the Laplace Tranform and by Numerical Method OBJECTIVES When you have completed thi chapter, you hould be able to: Find the

More information

Uniform Distribution. Uniform Distribution. Uniform Distribution. Graphs of Gamma Distributions. Gamma Distribution. Continuous Distributions CD - 1

Uniform Distribution. Uniform Distribution. Uniform Distribution. Graphs of Gamma Distributions. Gamma Distribution. Continuous Distributions CD - 1 Continuou Uniform Special Probability Denitie Definition 6.: The continuou random variable ha a uniform ditribution if it p.d.f. i equal to a contant on it upport. If the upport i the interval [a b] then

More information

Pythagorean Triple Updated 08--5 Drlnoordzij@leennoordzijnl wwwleennoordzijme Content A Roadmap for generating Pythagorean Triple Pythagorean Triple 3 Dicuion Concluion 5 A Roadmap for generating Pythagorean

More information

7.2 INVERSE TRANSFORMS AND TRANSFORMS OF DERIVATIVES 281

7.2 INVERSE TRANSFORMS AND TRANSFORMS OF DERIVATIVES 281 72 INVERSE TRANSFORMS AND TRANSFORMS OF DERIVATIVES 28 and i 2 Show how Euler formula (page 33) can then be ued to deduce the reult a ( a) 2 b 2 {e at co bt} {e at in bt} b ( a) 2 b 2 5 Under what condition

More information

PHYSICSBOWL March 29 April 14, 2017

PHYSICSBOWL March 29 April 14, 2017 PHYSICSBOWL 2017 March 29 April 14, 2017 40 QUESTIONS 45 MINUTES The ponor of the 2017 PhyicBowl, including the American Aociation of Phyic Teacher, are providing ome of the prize to recognize outtanding

More information

Review for Exam #1. Chapter 1. The Nature of Data. Definitions. Population. Sample. Quantitative data. Qualitative (attribute) data

Review for Exam #1. Chapter 1. The Nature of Data. Definitions. Population. Sample. Quantitative data. Qualitative (attribute) data Review for Exam #1 1 Chapter 1 Population the complete collection of elements (scores, people, measurements, etc.) to be studied Sample a subcollection of elements drawn from a population 11 The Nature

More information

2 Model-assisted and calibration estimators for finite population totals

2 Model-assisted and calibration estimators for finite population totals Int. Statitical Int.: Proc. 58th World Statitical Congre, 2011, Dublin (Seion CPS002) p.3847 Principal Component Regreion with Survey Data. Application on the French Media Audience Goga, Camelia IMB, Univerité

More information

Alternate Dispersion Measures in Replicated Factorial Experiments

Alternate Dispersion Measures in Replicated Factorial Experiments Alternate Diperion Meaure in Replicated Factorial Experiment Neal A. Mackertich The Raytheon Company, Sudbury MA 02421 Jame C. Benneyan Northeatern Univerity, Boton MA 02115 Peter D. Krau The Raytheon

More information