PhysicsAndMathsTutor.com

Size: px
Start display at page:

Download "PhysicsAndMathsTutor.com"

Transcription

1 1. A random sample of 100 people were asked f ther fnances were worse, the same or better than ths tme last year. The sample was splt accordng to ther annual ncome and the results are shown n the table below. Test, at the 5% level of sgnfcance, whether or not the relatve state of ther fnances s ndependent of ther ncome range. State your hypotheses and show your workng clearly. (Total 10 marks). A research worker studyng colour preference and the age of a random sample of 50 chldren obtaned the results shown below. Age n years Red Blue Totals Totals 8 50 Usng a 5% sgnfcance level, carry out a test to decde whether or not there s an assocaton between age and colour preference. State your hypotheses clearly. (Total 11 marks) dexcel Internal Revew 1

2 3. People over the age of 65 are offered an annual flu njecton. A health offcal took a random sample from a lst of patents who were over 65. She recorded ther gender and whether or not the offer of an annual flu njecton was accepted or rejected. The results are summarsed below. Gender Accepted Rejected Male Female Usng a 5% sgnfcance level, test whether or not there s an assocaton between gender and acceptance or rejecton of an annual flu njecton. State your hypotheses clearly. (Total 9 marks) 4. A researcher carred out a survey of three treatments for a frut tree dsease. The contngency table below shows the results of a survey of a random sample of 60 dseased trees. Tree ded wthn 1 year Tree survved for 1 4 years Tree survved beyond 4 years No acton Remove dseased branches Spray wth chemcals Test, at the 5% level of sgnfcance, whether or not there s any assocaton between the treatment of the trees and ther survval. State your hypotheses and concluson clearly. (Total 11 marks) dexcel Internal Revew

3 5. A random sample of 500 adults completed a questonnare on how often they took part n some form of exercse. They gave a response of never, sometmes or regularly. Of those asked, 5% were females of whom 10% never exercsed and 35% exercsed regularly. Of the males, 1.5% never exercsed and 55% sometmes exercsed. Test, at the 5% level of sgnfcance, whether or not there s any assocaton between gender and the amount of exercse. State your hypotheses clearly. (Total 1 marks) 6. A new drug to treat the common cold was used wth a randomly selected group of 100 volunteers. ach was gven the drug and ther health was montored to see f they caught a cold. A randomly selected control group of 100 volunteers was treated wth a dummy pll. The results are shown n the table below. Cold No cold Drug Dummy pll Usng a 5% sgnfcance level, test whether or not the chance of catchng a cold s affected by takng the new drug. State your hypotheses clearly. (Total 11 marks) dexcel Internal Revew 3

4 1. Fnances Worse Same Better Income Under M and above A H 0 : State of fnances and ncome are ndependent (not assocated) H 1 : State of fnances and ncome are not ndependent (assocated) O ( O ) O M A ( O ) O = or = = (awrt 3.55) A1 ν = ( 3 1)( 1) = cv s < so nsuffcent evdence to reject H 0 or not sgnfcant M1 There s no evdence of assocaton between state of fnances and ncome. A1 dexcel Internal Revew 4

5 Note 1 st Row Total Col.Total M1 for some use of. May be mpled by correct Grand Total 1 st A1 for all expected frequences correct for both hypotheses. Must menton state or fnances and ncome at least once Use of relatonshp or correlaton or connecton s B0 nd M1 for at least two correct terms (as n 3 rd or 4 th column) or correct expressons wth ther nd A1 for all correct terms. May be mpled by a correct answer. ( dp or better-allow eg 1.13 ) 3 rd M1 for a correct statement lnkng ther test statstc and ther cv. Must be χ not normal. 4 th A1 for a correct comment n context must menton state or fnances and ncome condone relatonshp or connecton here but not correlaton. No follow through. e.g. There s no evdence of a relatonshp between fnances and ncome [10]. H 0 : No assocaton between age and colour (Independent) H 1 : Assocaton between age and colour (Not ndependent) O (O-) at least one R T CT, 1 G T M1 A1 (O-) M1 A1 3 s.f. or better dexcel Internal Revew 5

6 (O-) awrt = , M1 A1.44 ν = (3 1)( 1) =, χ = ft Insuffcent evdence to reject H 0 No assocaton between age and colour A1ft [11] 3. H 0 : No assocaton between gender and acceptance H 1 : gender and acceptance are assocated Accept Not accept Total Males 170 (180) 110 (100) 80 Females 80 (70) 140 (150) 40 Totals xpected Values M1 A1 O ( O ) ( O ) =.59 (Yates.34) (Condone use of Yates ) M1 A1 ν = 1; (5%) = ; >.59. There s nsuffcent evdence to reject Ho M1 There s no assocaton between a persons gender and ther acceptance A1 (of the offer of a flu jab.) 9 [9] dexcel Internal Revew 6

7 4. No acton Remove dseased branches Spray wth Chemcals Totals Tree ded wthn 1 year 10(7) 5(7) 6(7) 1 Survved 1 4 years 5(7) 9(7) 7(7) 1 Survved > 4 years 5(6) 6(6) 7(6) 18 Totals RT CT GT M1 6 7 A1 3 6 A1 H 0 : Treatment & survval are ndependent (not assocated) H 1 : Treatment & survval are not ndependent (assocated) α = 0.05 = (3 1) (3 1) = 4 CR: χ > ( O ) = Use of ( O ) both ft M1 Any values = A1 awrt 3.48 A1 Snce s NOT n the crtcal regon (e < 9.488) there s nsuffcent evdence to reject H 0. There s no evdence of assocaton between treatment and length of survval. Comparson Concluson M1 A1ft [11] dexcel Internal Revew 7

8 5. Males Females Never Sometmes Regularly Totals M1 convert % to freq A1 (6, 91, 30, 13) A1 (143, 78) H 0 : No assocaton (ndependent) between gender and exercse H 1 : assocaton (not ndependent) between gender and exercse xpected Values Never Sometmes Regularly Totals Males Females M1 A1 at least 3sf α = 0.05 ν = ; CV χ > ; ft ( O ) O OR N = M1 A1 answers n range Not n crtcal regon no evdence of assocaton between A1 ft gender and exercse [1] dexcel Internal Revew 8

9 6. H 0 : Takng drug and catchng a cold are ndependent (not assocated) H 1 : Takng drug and catchng a cold are not ndependent (assocated)(not dtto) Both All totals RT CT = GT M1 A1 A1 Cold NoCold Drug 34(39.5) 66(60.5) 100 Dummy 45(39.5) 55(60.5) O ( O ) ( O ) =.53 (NB wth Yates.09) attempt & add, awrt & 0.5 twce, awrt.53 M1 A1 A1 v = 1, χ 1 (5%) = >.53 1, 3.841, No reason to beleve that the chance of catchng a cold s affected by takng the new drug A1 [11] dexcel Internal Revew 9

10 1. For most canddates ths queston was a good source of marks. Hypotheses were usually correctly phrased n terms of ndependence or assocaton and the calculatons were usually clearly set out although some napproprate roundng sometmes gave an answer of The degrees of freedom and crtcal value caused few problems and most gave a correct concluson n context.. There were some excellent responses wth a large number of correct answers seen. It was unusual not to see hypotheses well stated and the concluson gven correctly n context. 3. Ths queston was answered well and most canddates scored full or almost full marks. The xpected frequences were almost always correct as was the calculaton of the test statstc. Occasonally the hypotheses were the wrong way around and sometmes the concluson was not gven n context. 4. Many canddates produced completely correct solutons to ths queston. Ill-defned hypotheses, poor arthmetc and not gvng the concluson n context were the common errors. 5. Ths queston was very well done wth even very weak canddates managng to gan 11 or 1 marks. Some canddates tred to use percentages rather than frequences and a few dd not seem able to use the χ table correctly. As s often the case hypotheses were sometmes reversed. 6. There was a szeable group of canddates who answered ths queston well as they were able to demonstrate a good grasp of the mathematcal technque requred. The most common errors nvolved accuracy n the workng, careless defntons of the hypotheses and mssng context n the concluson. dexcel Internal Revew 10

Chi-squared tests 6E. So degrees of freedom=(3 1) (3 1)=4. Critical value is χ 4 2 (5%)=9.488

Chi-squared tests 6E. So degrees of freedom=(3 1) (3 1)=4. Critical value is χ 4 2 (5%)=9.488 Ch-squared tests 6 1 A 3 table has 3 rows (h) and columns (k) so: Degrees of freedom ν =(3 1)( 1)= Crtcal value s χ (5%)=5.991 H 0 : Ownershp s not related to the localty. H 1 : Ownershp s related to the

More information

As is less than , there is insufficient evidence to reject H 0 at the 5% level. The data may be modelled by Po(2).

As is less than , there is insufficient evidence to reject H 0 at the 5% level. The data may be modelled by Po(2). Ch-squared tests 6D 1 a H 0 : The data can be modelled by a Po() dstrbuton. H 1 : The data cannot be modelled by Po() dstrbuton. The observed and expected results are shown n the table. The last two columns

More information

Scatter Plot x

Scatter Plot x Construct a scatter plot usng excel for the gven data. Determne whether there s a postve lnear correlaton, negatve lnear correlaton, or no lnear correlaton. Complete the table and fnd the correlaton coeffcent

More information

Chapter 15 - Multiple Regression

Chapter 15 - Multiple Regression Chapter - Multple Regresson Chapter - Multple Regresson Multple Regresson Model The equaton that descrbes how the dependent varable y s related to the ndependent varables x, x,... x p and an error term

More information

x = , so that calculated

x = , so that calculated Stat 4, secton Sngle Factor ANOVA notes by Tm Plachowsk n chapter 8 we conducted hypothess tests n whch we compared a sngle sample s mean or proporton to some hypotheszed value Chapter 9 expanded ths to

More information

PhysicsAndMathsTutor.com

PhysicsAndMathsTutor.com 1. A total of 8 items are collected from an archaeological site. The distance from the centre of the site is recorded for each item. The results are summarised in the table below. Distance from the centre

More information

STATISTICS QUESTIONS. Step by Step Solutions.

STATISTICS QUESTIONS. Step by Step Solutions. STATISTICS QUESTIONS Step by Step Solutons www.mathcracker.com 9//016 Problem 1: A researcher s nterested n the effects of famly sze on delnquency for a group of offenders and examnes famles wth one to

More information

Statistics II Final Exam 26/6/18

Statistics II Final Exam 26/6/18 Statstcs II Fnal Exam 26/6/18 Academc Year 2017/18 Solutons Exam duraton: 2 h 30 mn 1. (3 ponts) A town hall s conductng a study to determne the amount of leftover food produced by the restaurants n the

More information

ANSWERS CHAPTER 9. TIO 9.2: If the values are the same, the difference is 0, therefore the null hypothesis cannot be rejected.

ANSWERS CHAPTER 9. TIO 9.2: If the values are the same, the difference is 0, therefore the null hypothesis cannot be rejected. ANSWERS CHAPTER 9 THINK IT OVER thnk t over TIO 9.: χ 2 k = ( f e ) = 0 e Breakng the equaton down: the test statstc for the ch-squared dstrbuton s equal to the sum over all categores of the expected frequency

More information

Lecture 9: Linear regression: centering, hypothesis testing, multiple covariates, and confounding

Lecture 9: Linear regression: centering, hypothesis testing, multiple covariates, and confounding Recall: man dea of lnear regresson Lecture 9: Lnear regresson: centerng, hypothess testng, multple covarates, and confoundng Sandy Eckel seckel@jhsph.edu 6 May 8 Lnear regresson can be used to study an

More information

Lecture 9: Linear regression: centering, hypothesis testing, multiple covariates, and confounding

Lecture 9: Linear regression: centering, hypothesis testing, multiple covariates, and confounding Lecture 9: Lnear regresson: centerng, hypothess testng, multple covarates, and confoundng Sandy Eckel seckel@jhsph.edu 6 May 008 Recall: man dea of lnear regresson Lnear regresson can be used to study

More information

Mark Scheme (Results) June 2008

Mark Scheme (Results) June 2008 Mark Scheme (Results) June 008 GCE GCE Mathematics (669101) Edexcel Limited. Registered in England and Wales No. 4496750 Registered Office: One90 High Holborn, London WC1V 7BH 1 June 008 6691 Statistics

More information

Lecture 6: Introduction to Linear Regression

Lecture 6: Introduction to Linear Regression Lecture 6: Introducton to Lnear Regresson An Manchakul amancha@jhsph.edu 24 Aprl 27 Lnear regresson: man dea Lnear regresson can be used to study an outcome as a lnear functon of a predctor Example: 6

More information

Department of Quantitative Methods & Information Systems. Time Series and Their Components QMIS 320. Chapter 6

Department of Quantitative Methods & Information Systems. Time Series and Their Components QMIS 320. Chapter 6 Department of Quanttatve Methods & Informaton Systems Tme Seres and Ther Components QMIS 30 Chapter 6 Fall 00 Dr. Mohammad Zanal These sldes were modfed from ther orgnal source for educatonal purpose only.

More information

Chapter 9: Statistical Inference and the Relationship between Two Variables

Chapter 9: Statistical Inference and the Relationship between Two Variables Chapter 9: Statstcal Inference and the Relatonshp between Two Varables Key Words The Regresson Model The Sample Regresson Equaton The Pearson Correlaton Coeffcent Learnng Outcomes After studyng ths chapter,

More information

Statistics for Economics & Business

Statistics for Economics & Business Statstcs for Economcs & Busness Smple Lnear Regresson Learnng Objectves In ths chapter, you learn: How to use regresson analyss to predct the value of a dependent varable based on an ndependent varable

More information

CHAPTER 6 GOODNESS OF FIT AND CONTINGENCY TABLE PREPARED BY: DR SITI ZANARIAH SATARI & FARAHANIM MISNI

CHAPTER 6 GOODNESS OF FIT AND CONTINGENCY TABLE PREPARED BY: DR SITI ZANARIAH SATARI & FARAHANIM MISNI CHAPTER 6 GOODNESS OF FIT AND CONTINGENCY TABLE Expected Outcomes Able to test the goodness of ft for categorcal data. Able to test whether the categorcal data ft to the certan dstrbuton such as Bnomal,

More information

Basic Business Statistics, 10/e

Basic Business Statistics, 10/e Chapter 13 13-1 Basc Busness Statstcs 11 th Edton Chapter 13 Smple Lnear Regresson Basc Busness Statstcs, 11e 009 Prentce-Hall, Inc. Chap 13-1 Learnng Objectves In ths chapter, you learn: How to use regresson

More information

1. Inference on Regression Parameters a. Finding Mean, s.d and covariance amongst estimates. 2. Confidence Intervals and Working Hotelling Bands

1. Inference on Regression Parameters a. Finding Mean, s.d and covariance amongst estimates. 2. Confidence Intervals and Working Hotelling Bands Content. Inference on Regresson Parameters a. Fndng Mean, s.d and covarance amongst estmates.. Confdence Intervals and Workng Hotellng Bands 3. Cochran s Theorem 4. General Lnear Testng 5. Measures of

More information

Modeling and Simulation NETW 707

Modeling and Simulation NETW 707 Modelng and Smulaton NETW 707 Lecture 5 Tests for Random Numbers Course Instructor: Dr.-Ing. Magge Mashaly magge.ezzat@guc.edu.eg C3.220 1 Propertes of Random Numbers Random Number Generators (RNGs) must

More information

Correlation and Regression. Correlation 9.1. Correlation. Chapter 9

Correlation and Regression. Correlation 9.1. Correlation. Chapter 9 Chapter 9 Correlaton and Regresson 9. Correlaton Correlaton A correlaton s a relatonshp between two varables. The data can be represented b the ordered pars (, ) where s the ndependent (or eplanator) varable,

More information

4 Analysis of Variance (ANOVA) 5 ANOVA. 5.1 Introduction. 5.2 Fixed Effects ANOVA

4 Analysis of Variance (ANOVA) 5 ANOVA. 5.1 Introduction. 5.2 Fixed Effects ANOVA 4 Analyss of Varance (ANOVA) 5 ANOVA 51 Introducton ANOVA ANOVA s a way to estmate and test the means of multple populatons We wll start wth one-way ANOVA If the populatons ncluded n the study are selected

More information

CHAPTER IV RESEARCH FINDING AND DISCUSSIONS

CHAPTER IV RESEARCH FINDING AND DISCUSSIONS CHAPTER IV RESEARCH FINDING AND DISCUSSIONS A. Descrpton of Research Fndng. The Implementaton of Learnng Havng ganed the whole needed data, the researcher then dd analyss whch refers to the statstcal data

More information

Department of Statistics University of Toronto STA305H1S / 1004 HS Design and Analysis of Experiments Term Test - Winter Solution

Department of Statistics University of Toronto STA305H1S / 1004 HS Design and Analysis of Experiments Term Test - Winter Solution Department of Statstcs Unversty of Toronto STA35HS / HS Desgn and Analyss of Experments Term Test - Wnter - Soluton February, Last Name: Frst Name: Student Number: Instructons: Tme: hours. Ads: a non-programmable

More information

PhysicsAndMathsTutor.com

PhysicsAndMathsTutor.com PhscsAndMathsTutor.com phscsandmathstutor.com June 005 5. The random varable X has probablt functon k, = 1,, 3, P( X = ) = k ( + 1), = 4, 5, where k s a constant. (a) Fnd the value of k. (b) Fnd the eact

More information

NANYANG TECHNOLOGICAL UNIVERSITY SEMESTER I EXAMINATION MTH352/MH3510 Regression Analysis

NANYANG TECHNOLOGICAL UNIVERSITY SEMESTER I EXAMINATION MTH352/MH3510 Regression Analysis NANYANG TECHNOLOGICAL UNIVERSITY SEMESTER I EXAMINATION 014-015 MTH35/MH3510 Regresson Analyss December 014 TIME ALLOWED: HOURS INSTRUCTIONS TO CANDIDATES 1. Ths examnaton paper contans FOUR (4) questons

More information

CHAPTER IV RESEARCH FINDING AND ANALYSIS

CHAPTER IV RESEARCH FINDING AND ANALYSIS CHAPTER IV REEARCH FINDING AND ANALYI A. Descrpton of Research Fndngs To fnd out the dfference between the students who were taught by usng Mme Game and the students who were not taught by usng Mme Game

More information

Statistics for Managers Using Microsoft Excel/SPSS Chapter 13 The Simple Linear Regression Model and Correlation

Statistics for Managers Using Microsoft Excel/SPSS Chapter 13 The Simple Linear Regression Model and Correlation Statstcs for Managers Usng Mcrosoft Excel/SPSS Chapter 13 The Smple Lnear Regresson Model and Correlaton 1999 Prentce-Hall, Inc. Chap. 13-1 Chapter Topcs Types of Regresson Models Determnng the Smple Lnear

More information

Methods in Epidemiology. Medical statistics 02/11/2014. Estimation How large is the effect? At the end of the lecture students should be able

Methods in Epidemiology. Medical statistics 02/11/2014. Estimation How large is the effect? At the end of the lecture students should be able Methods n Epdemology Estmaton How large s the effect? Medcal statstcs At the end of the lecture students should be able to llustrate the prncples of statstcal nference to nterpret confdence ntervals Methods

More information

Durban Watson for Testing the Lack-of-Fit of Polynomial Regression Models without Replications

Durban Watson for Testing the Lack-of-Fit of Polynomial Regression Models without Replications Durban Watson for Testng the Lack-of-Ft of Polynomal Regresson Models wthout Replcatons Ruba A. Alyaf, Maha A. Omar, Abdullah A. Al-Shha ralyaf@ksu.edu.sa, maomar@ksu.edu.sa, aalshha@ksu.edu.sa Department

More information

[The following data appear in Wooldridge Q2.3.] The table below contains the ACT score and college GPA for eight college students.

[The following data appear in Wooldridge Q2.3.] The table below contains the ACT score and college GPA for eight college students. PPOL 59-3 Problem Set Exercses n Smple Regresson Due n class /8/7 In ths problem set, you are asked to compute varous statstcs by hand to gve you a better sense of the mechancs of the Pearson correlaton

More information

Answers Problem Set 2 Chem 314A Williamsen Spring 2000

Answers Problem Set 2 Chem 314A Williamsen Spring 2000 Answers Problem Set Chem 314A Wllamsen Sprng 000 1) Gve me the followng crtcal values from the statstcal tables. a) z-statstc,-sded test, 99.7% confdence lmt ±3 b) t-statstc (Case I), 1-sded test, 95%

More information

Statistics for Business and Economics

Statistics for Business and Economics Statstcs for Busness and Economcs Chapter 11 Smple Regresson Copyrght 010 Pearson Educaton, Inc. Publshng as Prentce Hall Ch. 11-1 11.1 Overvew of Lnear Models n An equaton can be ft to show the best lnear

More information

CS-433: Simulation and Modeling Modeling and Probability Review

CS-433: Simulation and Modeling Modeling and Probability Review CS-433: Smulaton and Modelng Modelng and Probablty Revew Exercse 1. (Probablty of Smple Events) Exercse 1.1 The owner of a camera shop receves a shpment of fve cameras from a camera manufacturer. Unknown

More information

Mark Scheme (Results) Summer Pearson Edexcel GCE in Statistics 3 (6691/01)

Mark Scheme (Results) Summer Pearson Edexcel GCE in Statistics 3 (6691/01) Mark Scheme (Results) Summer 2014 Pearson Edexcel GCE in Statistics 3 (6691/01) Edexcel and BTEC Qualifications Edexcel and BTEC qualifications are awarded by Pearson, the UK s largest awarding body. We

More information

a. (All your answers should be in the letter!

a. (All your answers should be in the letter! Econ 301 Blkent Unversty Taskn Econometrcs Department of Economcs Md Term Exam I November 8, 015 Name For each hypothess testng n the exam complete the followng steps: Indcate the test statstc, ts crtcal

More information

Midterm Examination. Regression and Forecasting Models

Midterm Examination. Regression and Forecasting Models IOMS Department Regresson and Forecastng Models Professor Wllam Greene Phone: 22.998.0876 Offce: KMC 7-90 Home page: people.stern.nyu.edu/wgreene Emal: wgreene@stern.nyu.edu Course web page: people.stern.nyu.edu/wgreene/regresson/outlne.htm

More information

Here is the rationale: If X and y have a strong positive relationship to one another, then ( x x) will tend to be positive when ( y y)

Here is the rationale: If X and y have a strong positive relationship to one another, then ( x x) will tend to be positive when ( y y) Secton 1.5 Correlaton In the prevous sectons, we looked at regresson and the value r was a measurement of how much of the varaton n y can be attrbuted to the lnear relatonshp between y and x. In ths secton,

More information

DO NOT OPEN THE QUESTION PAPER UNTIL INSTRUCTED TO DO SO BY THE CHIEF INVIGILATOR. Introductory Econometrics 1 hour 30 minutes

DO NOT OPEN THE QUESTION PAPER UNTIL INSTRUCTED TO DO SO BY THE CHIEF INVIGILATOR. Introductory Econometrics 1 hour 30 minutes 25/6 Canddates Only January Examnatons 26 Student Number: Desk Number:...... DO NOT OPEN THE QUESTION PAPER UNTIL INSTRUCTED TO DO SO BY THE CHIEF INVIGILATOR Department Module Code Module Ttle Exam Duraton

More information

Rules of Probability

Rules of Probability ( ) ( ) = for all Corollary: Rules of robablty The probablty of the unon of any two events and B s roof: ( Φ) = 0. F. ( B) = ( ) + ( B) ( B) If B then, ( ) ( B). roof: week 2 week 2 2 Incluson / Excluson

More information

Statistics for Managers Using Microsoft Excel/SPSS Chapter 14 Multiple Regression Models

Statistics for Managers Using Microsoft Excel/SPSS Chapter 14 Multiple Regression Models Statstcs for Managers Usng Mcrosoft Excel/SPSS Chapter 14 Multple Regresson Models 1999 Prentce-Hall, Inc. Chap. 14-1 Chapter Topcs The Multple Regresson Model Contrbuton of Indvdual Independent Varables

More information

Chapter 5 Multilevel Models

Chapter 5 Multilevel Models Chapter 5 Multlevel Models 5.1 Cross-sectonal multlevel models 5.1.1 Two-level models 5.1.2 Multple level models 5.1.3 Multple level modelng n other felds 5.2 Longtudnal multlevel models 5.2.1 Two-level

More information

2016 Wiley. Study Session 2: Ethical and Professional Standards Application

2016 Wiley. Study Session 2: Ethical and Professional Standards Application 6 Wley Study Sesson : Ethcal and Professonal Standards Applcaton LESSON : CORRECTION ANALYSIS Readng 9: Correlaton and Regresson LOS 9a: Calculate and nterpret a sample covarance and a sample correlaton

More information

Topic- 11 The Analysis of Variance

Topic- 11 The Analysis of Variance Topc- 11 The Analyss of Varance Expermental Desgn The samplng plan or expermental desgn determnes the way that a sample s selected. In an observatonal study, the expermenter observes data that already

More information

Joint Statistical Meetings - Biopharmaceutical Section

Joint Statistical Meetings - Biopharmaceutical Section Iteratve Ch-Square Test for Equvalence of Multple Treatment Groups Te-Hua Ng*, U.S. Food and Drug Admnstraton 1401 Rockvlle Pke, #200S, HFM-217, Rockvlle, MD 20852-1448 Key Words: Equvalence Testng; Actve

More information

THE SUMMATION NOTATION Ʃ

THE SUMMATION NOTATION Ʃ Sngle Subscrpt otaton THE SUMMATIO OTATIO Ʃ Most of the calculatons we perform n statstcs are repettve operatons on lsts of numbers. For example, we compute the sum of a set of numbers, or the sum of the

More information

Statistical Evaluation of WATFLOOD

Statistical Evaluation of WATFLOOD tatstcal Evaluaton of WATFLD By: Angela MacLean, Dept. of Cvl & Envronmental Engneerng, Unversty of Waterloo, n. ctober, 005 The statstcs program assocated wth WATFLD uses spl.csv fle that s produced wth

More information

Chapter 14 Simple Linear Regression

Chapter 14 Simple Linear Regression Chapter 4 Smple Lnear Regresson Chapter 4 - Smple Lnear Regresson Manageral decsons often are based on the relatonshp between two or more varables. Regresson analss can be used to develop an equaton showng

More information

PROBABILITY PRIMER. Exercise Solutions

PROBABILITY PRIMER. Exercise Solutions PROBABILITY PRIMER Exercse Solutons 1 Probablty Prmer, Exercse Solutons, Prncples of Econometrcs, e EXERCISE P.1 (b) X s a random varable because attendance s not known pror to the outdoor concert. Before

More information

Lecture 6 More on Complete Randomized Block Design (RBD)

Lecture 6 More on Complete Randomized Block Design (RBD) Lecture 6 More on Complete Randomzed Block Desgn (RBD) Multple test Multple test The multple comparsons or multple testng problem occurs when one consders a set of statstcal nferences smultaneously. For

More information

PhysicsAndMathsTutor.com

PhysicsAndMathsTutor.com 1. An effect of a certain disease is that a small number of the red blood cells are deformed. Emily has this disease and the deformed blood cells occur randomly at a rate of 2.5 per ml of her blood. Following

More information

Lecture 16 Statistical Analysis in Biomaterials Research (Part II)

Lecture 16 Statistical Analysis in Biomaterials Research (Part II) 3.051J/0.340J 1 Lecture 16 Statstcal Analyss n Bomaterals Research (Part II) C. F Dstrbuton Allows comparson of varablty of behavor between populatons usng test of hypothess: σ x = σ x amed for Brtsh statstcan

More information

Economics 130. Lecture 4 Simple Linear Regression Continued

Economics 130. Lecture 4 Simple Linear Regression Continued Economcs 130 Lecture 4 Contnued Readngs for Week 4 Text, Chapter and 3. We contnue wth addressng our second ssue + add n how we evaluate these relatonshps: Where do we get data to do ths analyss? How do

More information

Econ107 Applied Econometrics Topic 3: Classical Model (Studenmund, Chapter 4)

Econ107 Applied Econometrics Topic 3: Classical Model (Studenmund, Chapter 4) I. Classcal Assumptons Econ7 Appled Econometrcs Topc 3: Classcal Model (Studenmund, Chapter 4) We have defned OLS and studed some algebrac propertes of OLS. In ths topc we wll study statstcal propertes

More information

Introduction to Econometrics (3 rd Updated Edition, Global Edition) Solutions to Odd-Numbered End-of-Chapter Exercises: Chapter 13

Introduction to Econometrics (3 rd Updated Edition, Global Edition) Solutions to Odd-Numbered End-of-Chapter Exercises: Chapter 13 Introducton to Econometrcs (3 rd Updated Edton, Global Edton by James H. Stock and Mark W. Watson Solutons to Odd-Numbered End-of-Chapter Exercses: Chapter 13 (Ths verson August 17, 014 Stock/Watson -

More information

: 5: ) A

: 5: ) A Revew 1 004.11.11 Chapter 1: 1. Elements, Varable, and Observatons:. Type o Data: Qualtatve Data and Quanttatve Data (a) Qualtatve data may be nonnumerc or numerc. (b) Quanttatve data are always numerc.

More information

Modelli Clamfim Equazione del Calore Lezione ottobre 2014

Modelli Clamfim Equazione del Calore Lezione ottobre 2014 CLAMFIM Bologna Modell 1 @ Clamfm Equazone del Calore Lezone 17 15 ottobre 2014 professor Danele Rtell danele.rtell@unbo.t 1/24? Convoluton The convoluton of two functons g(t) and f(t) s the functon (g

More information

/ n ) are compared. The logic is: if the two

/ n ) are compared. The logic is: if the two STAT C141, Sprng 2005 Lecture 13 Two sample tests One sample tests: examples of goodness of ft tests, where we are testng whether our data supports predctons. Two sample tests: called as tests of ndependence

More information

STAT 3008 Applied Regression Analysis

STAT 3008 Applied Regression Analysis STAT 3008 Appled Regresson Analyss Tutoral : Smple Lnear Regresson LAI Chun He Department of Statstcs, The Chnese Unversty of Hong Kong 1 Model Assumpton To quantfy the relatonshp between two factors,

More information

PhysicsAndMathsTutor.com

PhysicsAndMathsTutor.com 1. The product moment correlation coefficient is denoted by r and Spearman s rank correlation coefficient is denoted by r s. (a) Sketch separate scatter diagrams, with five points on each diagram, to show

More information

Methods of Detecting Outliers in A Regression Analysis Model.

Methods of Detecting Outliers in A Regression Analysis Model. Methods of Detectng Outlers n A Regresson Analyss Model. Ogu, A. I. *, Inyama, S. C+, Achugamonu, P. C++ *Department of Statstcs, Imo State Unversty,Owerr +Department of Mathematcs, Federal Unversty of

More information

BOOTSTRAP METHOD FOR TESTING OF EQUALITY OF SEVERAL MEANS. M. Krishna Reddy, B. Naveen Kumar and Y. Ramu

BOOTSTRAP METHOD FOR TESTING OF EQUALITY OF SEVERAL MEANS. M. Krishna Reddy, B. Naveen Kumar and Y. Ramu BOOTSTRAP METHOD FOR TESTING OF EQUALITY OF SEVERAL MEANS M. Krshna Reddy, B. Naveen Kumar and Y. Ramu Department of Statstcs, Osmana Unversty, Hyderabad -500 007, Inda. nanbyrozu@gmal.com, ramu0@gmal.com

More information

Online Appendix to: Axiomatization and measurement of Quasi-hyperbolic Discounting

Online Appendix to: Axiomatization and measurement of Quasi-hyperbolic Discounting Onlne Appendx to: Axomatzaton and measurement of Quas-hyperbolc Dscountng José Lus Montel Olea Tomasz Strzaleck 1 Sample Selecton As dscussed before our ntal sample conssts of two groups of subjects. Group

More information

Chapter 15 Student Lecture Notes 15-1

Chapter 15 Student Lecture Notes 15-1 Chapter 15 Student Lecture Notes 15-1 Basc Busness Statstcs (9 th Edton) Chapter 15 Multple Regresson Model Buldng 004 Prentce-Hall, Inc. Chap 15-1 Chapter Topcs The Quadratc Regresson Model Usng Transformatons

More information

STAT 3340 Assignment 1 solutions. 1. Find the equation of the line which passes through the points (1,1) and (4,5).

STAT 3340 Assignment 1 solutions. 1. Find the equation of the line which passes through the points (1,1) and (4,5). (out of 15 ponts) STAT 3340 Assgnment 1 solutons (10) (10) 1. Fnd the equaton of the lne whch passes through the ponts (1,1) and (4,5). β 1 = (5 1)/(4 1) = 4/3 equaton for the lne s y y 0 = β 1 (x x 0

More information

PhysicsAndMathsTutor.com

PhysicsAndMathsTutor.com 1. A researcher claims that, at a river bend, the water gradually gets deeper as the distance from the inner bank increases. He measures the distance from the inner bank, b cm, and the depth of a river,

More information

Evaluation of Validation Metrics. O. Polach Final Meeting Frankfurt am Main, September 27, 2013

Evaluation of Validation Metrics. O. Polach Final Meeting Frankfurt am Main, September 27, 2013 Evaluaton of Valdaton Metrcs O. Polach Fnal Meetng Frankfurt am Man, September 7, 013 Contents What s Valdaton Metrcs? Valdaton Metrcs evaluated n DynoTRAIN WP5 Drawbacks of Valdaton Metrcs Conclusons

More information

January Examinations 2015

January Examinations 2015 24/5 Canddates Only January Examnatons 25 DO NOT OPEN THE QUESTION PAPER UNTIL INSTRUCTED TO DO SO BY THE CHIEF INVIGILATOR STUDENT CANDIDATE NO.. Department Module Code Module Ttle Exam Duraton (n words)

More information

PhysicsAndMathsTutor.com

PhysicsAndMathsTutor.com 1. A researcher claims that, at a river bend, the water gradually gets deeper as the distance from the inner bank increases. He measures the distance from the inner bank, b cm, and the depth of a river,

More information

Spatial Statistics and Analysis Methods (for GEOG 104 class).

Spatial Statistics and Analysis Methods (for GEOG 104 class). Spatal Statstcs and Analyss Methods (for GEOG 104 class). Provded by Dr. An L, San Dego State Unversty. 1 Ponts Types of spatal data Pont pattern analyss (PPA; such as nearest neghbor dstance, quadrat

More information

Mark Scheme (Results) Summer Pearson Edexcel GCE Further Mathematics. Statistics S3 (6691)

Mark Scheme (Results) Summer Pearson Edexcel GCE Further Mathematics. Statistics S3 (6691) Mark Scheme (Results) Summer 017 Pearson dexcel GC Further Mathematics Statistics S3 (6691) dexcel and BTC Qualifications dexcel and BTC qualifications come from Pearson, the world s leading learning company.

More information

Statistics Chapter 4

Statistics Chapter 4 Statstcs Chapter 4 "There are three knds of les: les, damned les, and statstcs." Benjamn Dsrael, 1895 (Brtsh statesman) Gaussan Dstrbuton, 4-1 If a measurement s repeated many tmes a statstcal treatment

More information

Learning Objectives for Chapter 11

Learning Objectives for Chapter 11 Chapter : Lnear Regresson and Correlaton Methods Hldebrand, Ott and Gray Basc Statstcal Ideas for Managers Second Edton Learnng Objectves for Chapter Usng the scatterplot n regresson analyss Usng the method

More information

Comparison of Regression Lines

Comparison of Regression Lines STATGRAPHICS Rev. 9/13/2013 Comparson of Regresson Lnes Summary... 1 Data Input... 3 Analyss Summary... 4 Plot of Ftted Model... 6 Condtonal Sums of Squares... 6 Analyss Optons... 7 Forecasts... 8 Confdence

More information

Copyright 2010 Cengage Learning, Inc. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part.

Copyright 2010 Cengage Learning, Inc. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. yes to (3) two-sample problem? no to (4) underlyng dstrbuton normal or can centrallmt theorem be assumed to hold? and yes to (5) underlyng dstrbuton bnomal? We now refer to the flowchart at the end of

More information

UNIVERSITY OF TORONTO Faculty of Arts and Science. December 2005 Examinations STA437H1F/STA1005HF. Duration - 3 hours

UNIVERSITY OF TORONTO Faculty of Arts and Science. December 2005 Examinations STA437H1F/STA1005HF. Duration - 3 hours UNIVERSITY OF TORONTO Faculty of Arts and Scence December 005 Examnatons STA47HF/STA005HF Duraton - hours AIDS ALLOWED: (to be suppled by the student) Non-programmable calculator One handwrtten 8.5'' x

More information

STAT 511 FINAL EXAM NAME Spring 2001

STAT 511 FINAL EXAM NAME Spring 2001 STAT 5 FINAL EXAM NAME Sprng Instructons: Ths s a closed book exam. No notes or books are allowed. ou may use a calculator but you are not allowed to store notes or formulas n the calculator. Please wrte

More information

Biostatistics. Chapter 11 Simple Linear Correlation and Regression. Jing Li

Biostatistics. Chapter 11 Simple Linear Correlation and Regression. Jing Li Bostatstcs Chapter 11 Smple Lnear Correlaton and Regresson Jng L jng.l@sjtu.edu.cn http://cbb.sjtu.edu.cn/~jngl/courses/2018fall/b372/ Dept of Bonformatcs & Bostatstcs, SJTU Recall eat chocolate Cell 175,

More information

Section 8.3 Polar Form of Complex Numbers

Section 8.3 Polar Form of Complex Numbers 80 Chapter 8 Secton 8 Polar Form of Complex Numbers From prevous classes, you may have encountered magnary numbers the square roots of negatve numbers and, more generally, complex numbers whch are the

More information

ECONOMICS 351*-A Mid-Term Exam -- Fall Term 2000 Page 1 of 13 pages. QUEEN'S UNIVERSITY AT KINGSTON Department of Economics

ECONOMICS 351*-A Mid-Term Exam -- Fall Term 2000 Page 1 of 13 pages. QUEEN'S UNIVERSITY AT KINGSTON Department of Economics ECOOMICS 35*-A Md-Term Exam -- Fall Term 000 Page of 3 pages QUEE'S UIVERSITY AT KIGSTO Department of Economcs ECOOMICS 35* - Secton A Introductory Econometrcs Fall Term 000 MID-TERM EAM ASWERS MG Abbott

More information

A LINEAR PROGRAM TO COMPARE MULTIPLE GROSS CREDIT LOSS FORECASTS. Dr. Derald E. Wentzien, Wesley College, (302) ,

A LINEAR PROGRAM TO COMPARE MULTIPLE GROSS CREDIT LOSS FORECASTS. Dr. Derald E. Wentzien, Wesley College, (302) , A LINEAR PROGRAM TO COMPARE MULTIPLE GROSS CREDIT LOSS FORECASTS Dr. Derald E. Wentzen, Wesley College, (302) 736-2574, wentzde@wesley.edu ABSTRACT A lnear programmng model s developed and used to compare

More information

Formulas for the Determinant

Formulas for the Determinant page 224 224 CHAPTER 3 Determnants e t te t e 2t 38 A = e t 2te t e 2t e t te t 2e 2t 39 If 123 A = 345, 456 compute the matrx product A adj(a) What can you conclude about det(a)? For Problems 40 43, use

More information

x i1 =1 for all i (the constant ).

x i1 =1 for all i (the constant ). Chapter 5 The Multple Regresson Model Consder an economc model where the dependent varable s a functon of K explanatory varables. The economc model has the form: y = f ( x,x,..., ) xk Approxmate ths by

More information

Homework 10 Stat 547. Problem ) Z D!

Homework 10 Stat 547. Problem ) Z D! Homework 0 Stat 547 Problem 74 Notaton: h s the hazard rate for the aneulod grou, h s the hazard rate for the dlod grou (a Log-rank test s erformed: H 0 : h (t = h (t Sgnfcance level α = 005 Test statstc

More information

AS Statistics. SS03 Statistics 3 Mark scheme June Version 1.0: Final Mark Scheme

AS Statistics. SS03 Statistics 3 Mark scheme June Version 1.0: Final Mark Scheme AS Statistics SS03 Statistics 3 Mark scheme 6380 June 016 Version 1.0: Final Mark Scheme Mark schemes are prepared by the Lead Assessment Writer and considered, together with the relevant questions, by

More information

T E C O L O T E R E S E A R C H, I N C.

T E C O L O T E R E S E A R C H, I N C. T E C O L O T E R E S E A R C H, I N C. B rdg n g En g neern g a nd Econo mcs S nce 1973 THE MINIMUM-UNBIASED-PERCENTAGE ERROR (MUPE) METHOD IN CER DEVELOPMENT Thrd Jont Annual ISPA/SCEA Internatonal Conference

More information

Homework Assignment 3 Due in class, Thursday October 15

Homework Assignment 3 Due in class, Thursday October 15 Homework Assgnment 3 Due n class, Thursday October 15 SDS 383C Statstcal Modelng I 1 Rdge regresson and Lasso 1. Get the Prostrate cancer data from http://statweb.stanford.edu/~tbs/elemstatlearn/ datasets/prostate.data.

More information

ISQS 6348 Final Open notes, no books. Points out of 100 in parentheses. Y 1 ε 2

ISQS 6348 Final Open notes, no books. Points out of 100 in parentheses. Y 1 ε 2 ISQS 6348 Fnal Open notes, no books. Ponts out of 100 n parentheses. 1. The followng path dagram s gven: ε 1 Y 1 ε F Y 1.A. (10) Wrte down the usual model and assumptons that are mpled by ths dagram. Soluton:

More information

Chapter 3 Describing Data Using Numerical Measures

Chapter 3 Describing Data Using Numerical Measures Chapter 3 Student Lecture Notes 3-1 Chapter 3 Descrbng Data Usng Numercal Measures Fall 2006 Fundamentals of Busness Statstcs 1 Chapter Goals To establsh the usefulness of summary measures of data. The

More information

THE CHINESE REMAINDER THEOREM. We should thank the Chinese for their wonderful remainder theorem. Glenn Stevens

THE CHINESE REMAINDER THEOREM. We should thank the Chinese for their wonderful remainder theorem. Glenn Stevens THE CHINESE REMAINDER THEOREM KEITH CONRAD We should thank the Chnese for ther wonderful remander theorem. Glenn Stevens 1. Introducton The Chnese remander theorem says we can unquely solve any par of

More information

ANSWERS. Problem 1. and the moment generating function (mgf) by. defined for any real t. Use this to show that E( U) var( U)

ANSWERS. Problem 1. and the moment generating function (mgf) by. defined for any real t. Use this to show that E( U) var( U) Econ 413 Exam 13 H ANSWERS Settet er nndelt 9 deloppgaver, A,B,C, som alle anbefales å telle lkt for å gøre det ltt lettere å stå. Svar er gtt . Unfortunately, there s a prntng error n the hnt of

More information

Mark Scheme (Results) Summer 2007

Mark Scheme (Results) Summer 2007 Mark Scheme (Results) Summer 007 GCE GCE Mathematics Statistics S3 (6691) Edexcel Limited. Registered in England and Wales No. 4496750 Registered Office: One90 High Holborn, London WC1V 7BH June 007 6691

More information

Exercises of Chapter 2

Exercises of Chapter 2 Exercses of Chapter Chuang-Cheh Ln Department of Computer Scence and Informaton Engneerng, Natonal Chung Cheng Unversty, Mng-Hsung, Chay 61, Tawan. Exercse.6. Suppose that we ndependently roll two standard

More information

AS-Level Maths: Statistics 1 for Edexcel

AS-Level Maths: Statistics 1 for Edexcel 1 of 6 AS-Level Maths: Statstcs 1 for Edecel S1. Calculatng means and standard devatons Ths con ndcates the slde contans actvtes created n Flash. These actvtes are not edtable. For more detaled nstructons,

More information

CHAPTER 8. Exercise Solutions

CHAPTER 8. Exercise Solutions CHAPTER 8 Exercse Solutons 77 Chapter 8, Exercse Solutons, Prncples of Econometrcs, 3e 78 EXERCISE 8. When = N N N ( x x) ( x x) ( x x) = = = N = = = N N N ( x ) ( ) ( ) ( x x ) x x x x x = = = = Chapter

More information

Improving subjective estimations using the paired comparisons method

Improving subjective estimations using the paired comparisons method Improvng subectve estmatons usng the pared comparsons method Internatonal Forum on COCOMO and Software Cost Estmaton Eduardo Mranda October 24-27, 2000 Agenda Subectve estmates Estmatng person-hours Can

More information

PBAF 528 Week Theory Is the variable s place in the equation certain and theoretically sound? Most important! 2. T-test

PBAF 528 Week Theory Is the variable s place in the equation certain and theoretically sound? Most important! 2. T-test PBAF 528 Week 6 How do we choose our model? How do you decde whch ndependent varables? If you want to read more about ths, try Studenmund, A.H. Usng Econometrcs Chapter 7. (ether 3 rd or 4 th Edtons) 1.

More information

EXAMINATION. N0028N Econometrics. Luleå University of Technology. Date: (A1016) Time: Aid: Calculator and dictionary

EXAMINATION. N0028N Econometrics. Luleå University of Technology. Date: (A1016) Time: Aid: Calculator and dictionary EXAMINATION Luleå Unversty of Technology N008N Econometrcs Date: 011-05-16 (A1016) Tme: 09.00-13.00 Ad: Calculator and dctonary Teacher on duty (complete telephone number) Robert Lundmark (070-1735788)

More information

Lecture 4 Hypothesis Testing

Lecture 4 Hypothesis Testing Lecture 4 Hypothess Testng We may wsh to test pror hypotheses about the coeffcents we estmate. We can use the estmates to test whether the data rejects our hypothess. An example mght be that we wsh to

More information

Basically, if you have a dummy dependent variable you will be estimating a probability.

Basically, if you have a dummy dependent variable you will be estimating a probability. ECON 497: Lecture Notes 13 Page 1 of 1 Metropoltan State Unversty ECON 497: Research and Forecastng Lecture Notes 13 Dummy Dependent Varable Technques Studenmund Chapter 13 Bascally, f you have a dummy

More information