G r a d e. 2 M a t h e M a t i c s. statistics and Probability

Size: px
Start display at page:

Download "G r a d e. 2 M a t h e M a t i c s. statistics and Probability"

Transcription

1 G r a d e 2 M a t h e M a t i c s statistics ad Probability

2

3 Grade 2: Statistics (Data Aalysis) (2.SP.1, 2.SP.2) edurig uderstadigs: data ca be collected ad orgaized i a variety of ways. data ca be used to aswer questios. essetial Questios: Why do we collect data? how ca data be collected ad recorded? SPecific LeAriG outcome(s): Achievemet idicators: 2.SP.1 Gather ad record data about self ad others to aswer questios. [C, CN, PS, V] 2.SP.2 Costruct ad iterpret cocrete graphs ad pictographs to solve problems. [C, CN, PS, R, V] Formulate a questio that ca be aswered by gatherig iformatio about self ad others. Orgaize data as it is collected usig cocrete objects, tallies, checkmarks, charts, or lists. Aswer questios usig collected data. Determie the commo attributes of cocrete graphs by comparig a set of cocrete graphs. Determie the commo attributes of pictographs by comparig a set of pictographs. Aswer questios pertaiig to a cocrete graph or pictograph. Create a cocrete graph to display a set of data ad draw coclusios. Create a pictograph to represet a set of data usig oe-to-oe correspodece. Solve a problem by costructig ad iterpretig a cocrete graph or pictograph. s t a t i s t i c s a d P r o b a b i l i t y ( d a t a a a l y s i s ) 3

4 Prior Kowledge Studets may have had o formal istructio i statistics. BacKgroud iformatio A pictograph uses uiform, represetative pictures to depict quatities of objects or people. It is used whe the data are discrete (o-cotiuous). The symbols used must be the same size ad shape to avoid misleadig the audiece. Example of a pictograph: Pictographs eed to have a title, labels, ad pictures. Legeds/keys are eeded whe the pictures or symbols are used to represet more tha oe quatity (may-to-oe correspodece). A cocrete graph is made usig the actual objects or people o a graphig mat. Cocrete graphs eed to have a title ad labels. A graphig mat is made from thick plastic sheetig (the type that ca be bought off the roll at a hardware store). Oe side has squares large eough for a perso to stad o. These are made usig maskig tape. There is usually room for three colums ad 10 rows. The secod side has tile-sized squares agai made with maskig tape. There is usually room for five colums ad at least 12 rows. 4 G r a d e 2 M a t h e m a t i c s : s u p p o r t d o c u m e t f o r t e a c h e r s

5 As childre collect objects, they aturally sort, cout, ad compare. Sortig, coutig, ad comparig are the basis for uderstadig statistics. Childre also aturally ask questios to gather iformatio. Teachers ca use classroom experieces as sources of iformatio to capitalize o childre s iterests ad to help them see that statistics are a part of everyday life. As well, data collectio provides a way to coect mathematics to other subject areas. Good questios are a itegral part of data collectio. Studets eed practice formulatig questios i more tha oe way. By examiig the possible aswers to a set of similar questios, studets ca determie which oe will best provide the desired data. Note: Surveys should be made maageable by obtaiig iformatio from a small populatio (e.g., o larger tha a sigle class), ad by limitig the umber of categories to two or three. Teachers eed to model ad develop the laguage of statistics i oral ad writte formats. mathematical laguage categories match least label more cocrete graph title less pictograph data same amout as compare tallies most survey learig experieces Assessig Prior Kowledge Preset a questio such as, Do you have a pet at home? Ask studets what aswers are possible (yes or o). Ask for suggestios as to how you might gather the iformatio. The suggestios they make should reflect their prior experiece. Complete the survey ad discuss the results. s t a t i s t i c s a d P r o b a b i l i t y ( d a t a a a l y s i s ) 5

6 formulate a questio that ca be aswered by gatherig iformatio about self ad others. orgaize data as it is collected usig cocrete objects, tallies, checkmarks, charts, or lists. Aswer questios usig collected data. suggestios for istructio Read a book such as The Best Vacatio Ever by Stuart J. Murphy or Charlie s Checklist by Rory S. Lerma. Both books have characters that formulate questios ad gather data. Discuss the questios chose ad the methods used to gather ad record the data. Model the formulatio of questios, such as I woder... How ca we fid out? Whom shall we ask? Use everyday occurreces to formulate questios about the childre s eviromet. Sample questios: How do you travel to school? Which kid of pizza did you order? Which author should we read this week? How may times ca you hop o oe foot? What is your favourite aimal? Model questios o the same topic i several ways ad allow the group to choose the best questio for its purpose. This is a importat process of data collectio that they will eed to practise. Examples: How did you travel to school today? Did you walk to school today? How may childre i our class used the school bus today? 6 G r a d e 2 M a t h e m a t i c s : s u p p o r t d o c u m e t f o r t e a c h e r s

7 Select a survey questio that ca be aswered yes or o. Model ways i which the data ca be collected. Examples: two differet colours of uifix cubes (oe for yes ad the other for o ) tallies a class list ad writig yes or o beside each perso s ame checkmarks Have studets aswer questios about the data. Examples: Which oe has the most/least? How may more? How may less? How may people were surveyed altogether? Math Routie: Questio of the Week Have pairs of studets take turs formulatig a survey questio, collectig the data, represetig it, ad the presetig their fidigs to the class. This ca be used as formative assessmet. Assessig Uderstadig Studets work i pairs. Have each group formulate a survey questio, collect the data, ad summarize the results by makig statemets about the data. s t a t i s t i c s a d P r o b a b i l i t y ( d a t a a a l y s i s ) 7

8 Determie the commo attributes of cocrete graphs ad of pictographs by comparig a set of cocrete graphs ad a set of pictographs respectively. Aswer questios pertaiig to a cocrete graph or pictograph. create a cocrete graph to display a set of data ad draw coclusios. create a pictograph to represet a set of data usig oe-to-oe correspodece. Solve a problem by costructig ad iterpretig a cocrete graph or pictograph. suggestios for istructio Model the costructio of a cocrete graph. Have studets formulate a questio ad decide the aswer choices. Make a label for each choice. Decide o a title for the graph ad write it o a strip of paper. Place the labels at the bottom of each colum of the graphig mat ad the title at the top or o the side. Have studets stad i the appropriate colum. Studets eed to be show how to lie up o the Idividual graphig mats ca be made from checkered or striped viyl. graphig mat start at the bottom, oe perso i each square, do ot skip squares. Discuss the results. Demostrate the use of oe-to-oe matchig as a strategy for comparig the data. Have studets hold hads with someoe from the ext colum ad cout studets without parters to determie the differece. Note: Data ca be trasferred to the pictograph usig a small grid ad studet pictures. Cocrete Graph Do You Like Pizza? No Yes Pictograph Note: Although pictographs cosist of uiform pictures, use studet photographs o a similar backgroud to make a class pictograph. 8 G r a d e 2 M a t h e m a t i c s : s u p p o r t d o c u m e t f o r t e a c h e r s

9 Use coloured cubes, pasta, cereal, or cadies, ad a small graphig mat. From the collectio of objects have studets select oe that represets their favourite colour. Make colour labels ad a title for the graph. Have studets place their object i the correct colum o the graph. Chage the cocrete graph to a pictograph by havig studets substitute a coloured square or circle for the actual object. Discuss the similarities ad differeces betwee the two graph types. Have studets create three differet represetatios of the same set of data. Example: Glue coloured pasta o the first grid. O the secod grid, draw ad color pasta pieces to represet the same iformatio that is o the first oe. Lastly, represet the same data with tallies. Discuss how the three grids are the same ad how they are differet. Costruct two differet cocrete graphs. Have studets compare the graphs ad idetify the commo attributes (title, labels). Costruct two differet pictographs. Have studets compare the graphs ad idetify the commo attributes (title, labels). I preparatio for the iterpretatio of data, lead studets to ask ad aswer questios about the iformatio o graphs. Example: Sample questios: What does the pictograph show? How do you kow? What does this tell about the colours of apples? Which do we like most? least? How may more are there of our most favourite colour tha our least favourite colour? Which do we like more yellow or gree? How do you kow? How may people were surveyed? How do you kow? s t a t i s t i c s a d P r o b a b i l i t y ( d a t a a a l y s i s ) 9

10 Provide meaigful opportuities for studets to collect, represet, ad iterpret data. Examples: vote o a class book to read collect data o the umber of suy, cloudy, raiy, sowy days i a particular moth decide o a game to play for idoor recess The sciece, social studies, ad health curricula provide meaigful cotexts for workig with data. Assessig Uderstadig 1. Show studets a cocrete graph or a pictograph. Have studets describe, orally or i writig, what the graph is showig (iterpret the data). 2. Give studets a set of data. Example: Do you have a cat? Yes No Have studets costruct a cocrete graph or pictograph usig the data. 10 G r a d e 2 M a t h e m a t i c s : s u p p o r t d o c u m e t f o r t e a c h e r s

11 Puttig the Pieces together Plaig a class celebratio Cotext Tell studets that they are goig to be plaig a class celebratio/special evet. Have them braistorm for thigs they would like to have at the celebratio (food, games, beverage, music, movie, etc.). Have studets work i parters or small groups. Assig each group (or have groups select) a category from the braistormed list. Have each group formulate a questio determie the aswer choices collect the data represet the data i graph form summarize the data i writte form preset the results to the class s t a t i s t i c s a d P r o b a b i l i t y ( d a t a a a l y s i s ) 11

12 N o t e s 12 G r a d e 2 M a t h e m a t i c s : s u p p o r t d o c u m e t f o r t e a c h e r s

Mathematical goals. Starting points. Materials required. Time needed

Mathematical goals. Starting points. Materials required. Time needed Level A1 of challege: C A1 Mathematical goals Startig poits Materials required Time eeded Iterpretig algebraic expressios To help learers to: traslate betwee words, symbols, tables, ad area represetatios

More information

CHAPTER 7: Central Limit Theorem: CLT for Averages (Means)

CHAPTER 7: Central Limit Theorem: CLT for Averages (Means) CHAPTER 7: Cetral Limit Theorem: CLT for Averages (Meas) X = the umber obtaied whe rollig oe six sided die oce. If we roll a six sided die oce, the mea of the probability distributio is X P(X = x) Simulatio:

More information

SECTION 1.5 : SUMMATION NOTATION + WORK WITH SEQUENCES

SECTION 1.5 : SUMMATION NOTATION + WORK WITH SEQUENCES SECTION 1.5 : SUMMATION NOTATION + WORK WITH SEQUENCES Read Sectio 1.5 (pages 5 9) Overview I Sectio 1.5 we lear to work with summatio otatio ad formulas. We will also itroduce a brief overview of sequeces,

More information

Overview. Learning Objectives. Point Estimate. Estimation. Estimating the Value of a Parameter Using Confidence Intervals

Overview. Learning Objectives. Point Estimate. Estimation. Estimating the Value of a Parameter Using Confidence Intervals Overview Estimatig the Value of a Parameter Usig Cofidece Itervals We apply the results about the sample mea the problem of estimatio Estimatio is the process of usig sample data estimate the value of

More information

7.1 Finding Rational Solutions of Polynomial Equations

7.1 Finding Rational Solutions of Polynomial Equations 4 Locker LESSON 7. Fidig Ratioal Solutios of Polyomial Equatios Name Class Date 7. Fidig Ratioal Solutios of Polyomial Equatios Essetial Questio: How do you fid the ratioal roots of a polyomial equatio?

More information

Measures of Spread and Boxplots Discrete Math, Section 9.4

Measures of Spread and Boxplots Discrete Math, Section 9.4 Measures of Spread ad Boxplots Discrete Math, Sectio 9.4 We start with a example: Example 1: Comparig Mea ad Media Compute the mea ad media of each data set: S 1 = {4, 6, 8, 10, 1, 14, 16} S = {4, 7, 9,

More information

The following example will help us understand The Sampling Distribution of the Mean. C1 C2 C3 C4 C5 50 miles 84 miles 38 miles 120 miles 48 miles

The following example will help us understand The Sampling Distribution of the Mean. C1 C2 C3 C4 C5 50 miles 84 miles 38 miles 120 miles 48 miles The followig eample will help us uderstad The Samplig Distributio of the Mea Review: The populatio is the etire collectio of all idividuals or objects of iterest The sample is the portio of the populatio

More information

Week 3 Conditional probabilities, Bayes formula, WEEK 3 page 1 Expected value of a random variable

Week 3 Conditional probabilities, Bayes formula, WEEK 3 page 1 Expected value of a random variable Week 3 Coditioal probabilities, Bayes formula, WEEK 3 page 1 Expected value of a radom variable We recall our discussio of 5 card poker hads. Example 13 : a) What is the probability of evet A that a 5

More information

Biology 171L Environment and Ecology Lab Lab 2: Descriptive Statistics, Presenting Data and Graphing Relationships

Biology 171L Environment and Ecology Lab Lab 2: Descriptive Statistics, Presenting Data and Graphing Relationships Biology 171L Eviromet ad Ecology Lab Lab : Descriptive Statistics, Presetig Data ad Graphig Relatioships Itroductio Log lists of data are ofte ot very useful for idetifyig geeral treds i the data or the

More information

FOUNDATIONS OF MATHEMATICS AND PRE-CALCULUS GRADE 10

FOUNDATIONS OF MATHEMATICS AND PRE-CALCULUS GRADE 10 FOUNDATIONS OF MATHEMATICS AND PRE-CALCULUS GRADE 10 [C] Commuicatio Measuremet A1. Solve problems that ivolve liear measuremet, usig: SI ad imperial uits of measure estimatio strategies measuremet strategies.

More information

Professional Networking

Professional Networking Professioal Networkig 1. Lear from people who ve bee where you are. Oe of your best resources for etworkig is alumi from your school. They ve take the classes you have take, they have bee o the job market

More information

5.4 Amortization. Question 1: How do you find the present value of an annuity? Question 2: How is a loan amortized?

5.4 Amortization. Question 1: How do you find the present value of an annuity? Question 2: How is a loan amortized? 5.4 Amortizatio Questio 1: How do you fid the preset value of a auity? Questio 2: How is a loa amortized? Questio 3: How do you make a amortizatio table? Oe of the most commo fiacial istrumets a perso

More information

insight reporting solutions

insight reporting solutions reportig solutios Create ad cotrol olie customized score reports to measure studet progress ad to determie ways to improve istructio. isight Customized Reportig empowers you to make data-drive decisios.

More information

5: Introduction to Estimation

5: Introduction to Estimation 5: Itroductio to Estimatio Cotets Acroyms ad symbols... 1 Statistical iferece... Estimatig µ with cofidece... 3 Samplig distributio of the mea... 3 Cofidece Iterval for μ whe σ is kow before had... 4 Sample

More information

Hypergeometric Distributions

Hypergeometric Distributions 7.4 Hypergeometric Distributios Whe choosig the startig lie-up for a game, a coach obviously has to choose a differet player for each positio. Similarly, whe a uio elects delegates for a covetio or you

More information

PUBLIC RELATIONS PROJECT 2016

PUBLIC RELATIONS PROJECT 2016 PUBLIC RELATIONS PROJECT 2016 The purpose of the Public Relatios Project is to provide a opportuity for the chapter members to demostrate the kowledge ad skills eeded i plaig, orgaizig, implemetig ad evaluatig

More information

How To Solve The Homewor Problem Beautifully

How To Solve The Homewor Problem Beautifully Egieerig 33 eautiful Homewor et 3 of 7 Kuszmar roblem.5.5 large departmet store sells sport shirts i three sizes small, medium, ad large, three patters plaid, prit, ad stripe, ad two sleeve legths log

More information

CHAPTER 3 THE TIME VALUE OF MONEY

CHAPTER 3 THE TIME VALUE OF MONEY CHAPTER 3 THE TIME VALUE OF MONEY OVERVIEW A dollar i the had today is worth more tha a dollar to be received i the future because, if you had it ow, you could ivest that dollar ad ear iterest. Of all

More information

Chapter 5 Unit 1. IET 350 Engineering Economics. Learning Objectives Chapter 5. Learning Objectives Unit 1. Annual Amount and Gradient Functions

Chapter 5 Unit 1. IET 350 Engineering Economics. Learning Objectives Chapter 5. Learning Objectives Unit 1. Annual Amount and Gradient Functions Chapter 5 Uit Aual Amout ad Gradiet Fuctios IET 350 Egieerig Ecoomics Learig Objectives Chapter 5 Upo completio of this chapter you should uderstad: Calculatig future values from aual amouts. Calculatig

More information

FM4 CREDIT AND BORROWING

FM4 CREDIT AND BORROWING FM4 CREDIT AND BORROWING Whe you purchase big ticket items such as cars, boats, televisios ad the like, retailers ad fiacial istitutios have various terms ad coditios that are implemeted for the cosumer

More information

Analyzing Longitudinal Data from Complex Surveys Using SUDAAN

Analyzing Longitudinal Data from Complex Surveys Using SUDAAN Aalyzig Logitudial Data from Complex Surveys Usig SUDAAN Darryl Creel Statistics ad Epidemiology, RTI Iteratioal, 312 Trotter Farm Drive, Rockville, MD, 20850 Abstract SUDAAN: Software for the Statistical

More information

Lesson 17 Pearson s Correlation Coefficient

Lesson 17 Pearson s Correlation Coefficient Outlie Measures of Relatioships Pearso s Correlatio Coefficiet (r) -types of data -scatter plots -measure of directio -measure of stregth Computatio -covariatio of X ad Y -uique variatio i X ad Y -measurig

More information

1 Computing the Standard Deviation of Sample Means

1 Computing the Standard Deviation of Sample Means Computig the Stadard Deviatio of Sample Meas Quality cotrol charts are based o sample meas ot o idividual values withi a sample. A sample is a group of items, which are cosidered all together for our aalysis.

More information

CS100: Introduction to Computer Science

CS100: Introduction to Computer Science Review: History of Computers CS100: Itroductio to Computer Sciece Maiframes Miicomputers Lecture 2: Data Storage -- Bits, their storage ad mai memory Persoal Computers & Workstatios Review: The Role of

More information

Laws of Exponents Learning Strategies

Laws of Exponents Learning Strategies Laws of Epoets Learig Strategies What should studets be able to do withi this iteractive? Studets should be able to uderstad ad use of the laws of epoets. Studets should be able to simplify epressios that

More information

Inference on Proportion. Chapter 8 Tests of Statistical Hypotheses. Sampling Distribution of Sample Proportion. Confidence Interval

Inference on Proportion. Chapter 8 Tests of Statistical Hypotheses. Sampling Distribution of Sample Proportion. Confidence Interval Chapter 8 Tests of Statistical Hypotheses 8. Tests about Proportios HT - Iferece o Proportio Parameter: Populatio Proportio p (or π) (Percetage of people has o health isurace) x Statistic: Sample Proportio

More information

Elementary Theory of Russian Roulette

Elementary Theory of Russian Roulette Elemetary Theory of Russia Roulette -iterestig patters of fractios- Satoshi Hashiba Daisuke Miematsu Ryohei Miyadera Itroductio. Today we are goig to study mathematical theory of Russia roulette. If some

More information

COMPARISON SHOPPING. 7 Store brands or generic brands usually cost. less than name or national brands. 7 Generic brand foods are safe and good for

COMPARISON SHOPPING. 7 Store brands or generic brands usually cost. less than name or national brands. 7 Generic brand foods are safe and good for COMPARISON SHOPPING Makig smart buys at the grocery store is as easy as learig a few shoppig skills ad makig smart decisios. Before buyig a item, thik about price, coveiece, utritioal value, how you will

More information

Taking DCOP to the Real World: Efficient Complete Solutions for Distributed Multi-Event Scheduling

Taking DCOP to the Real World: Efficient Complete Solutions for Distributed Multi-Event Scheduling Taig DCOP to the Real World: Efficiet Complete Solutios for Distributed Multi-Evet Schedulig Rajiv T. Maheswara, Milid Tambe, Emma Bowrig, Joatha P. Pearce, ad Pradeep araatham Uiversity of Souther Califoria

More information

Lesson 15 ANOVA (analysis of variance)

Lesson 15 ANOVA (analysis of variance) Outlie Variability -betwee group variability -withi group variability -total variability -F-ratio Computatio -sums of squares (betwee/withi/total -degrees of freedom (betwee/withi/total -mea square (betwee/withi

More information

G r a d e. 5 M a t h e M a t i c s. Number

G r a d e. 5 M a t h e M a t i c s. Number G r a d e 5 M a t h e M a t i c s Number Grade 5: Number (5.N.1) edurig uderstadigs: the positio of a digit i a umber determies its value. each place value positio is 10 times greater tha the place value

More information

The Forgotten Middle. research readiness results. Executive Summary

The Forgotten Middle. research readiness results. Executive Summary The Forgotte Middle Esurig that All Studets Are o Target for College ad Career Readiess before High School Executive Summary Today, college readiess also meas career readiess. While ot every high school

More information

Handling. Collection Calls

Handling. Collection Calls Hadlig the Collectio Calls We do everythig we ca to stop collectio calls; however, i the early part of our represetatio, you ca expect some of these calls to cotiue. We uderstad that the first few moths

More information

I. Chi-squared Distributions

I. Chi-squared Distributions 1 M 358K Supplemet to Chapter 23: CHI-SQUARED DISTRIBUTIONS, T-DISTRIBUTIONS, AND DEGREES OF FREEDOM To uderstad t-distributios, we first eed to look at aother family of distributios, the chi-squared distributios.

More information

Department of Computer Science, University of Otago

Department of Computer Science, University of Otago Departmet of Computer Sciece, Uiversity of Otago Techical Report OUCS-2006-09 Permutatios Cotaiig May Patters Authors: M.H. Albert Departmet of Computer Sciece, Uiversity of Otago Micah Colema, Rya Fly

More information

THE REGRESSION MODEL IN MATRIX FORM. For simple linear regression, meaning one predictor, the model is. for i = 1, 2, 3,, n

THE REGRESSION MODEL IN MATRIX FORM. For simple linear regression, meaning one predictor, the model is. for i = 1, 2, 3,, n We will cosider the liear regressio model i matrix form. For simple liear regressio, meaig oe predictor, the model is i = + x i + ε i for i =,,,, This model icludes the assumptio that the ε i s are a sample

More information

CS103X: Discrete Structures Homework 4 Solutions

CS103X: Discrete Structures Homework 4 Solutions CS103X: Discrete Structures Homewor 4 Solutios Due February 22, 2008 Exercise 1 10 poits. Silico Valley questios: a How may possible six-figure salaries i whole dollar amouts are there that cotai at least

More information

1 Correlation and Regression Analysis

1 Correlation and Regression Analysis 1 Correlatio ad Regressio Aalysis I this sectio we will be ivestigatig the relatioship betwee two cotiuous variable, such as height ad weight, the cocetratio of a ijected drug ad heart rate, or the cosumptio

More information

WindWise Education. 2 nd. T ransforming the Energy of Wind into Powerful Minds. editi. A Curriculum for Grades 6 12

WindWise Education. 2 nd. T ransforming the Energy of Wind into Powerful Minds. editi. A Curriculum for Grades 6 12 WidWise Educatio T rasformig the Eergy of Wid ito Powerful Mids A Curriculum for Grades 6 12 Notice Except for educatioal use by a idividual teacher i a classroom settig this work may ot be reproduced

More information

Solving equations. Pre-test. Warm-up

Solving equations. Pre-test. Warm-up Solvig equatios 8 Pre-test Warm-up We ca thik of a algebraic equatio as beig like a set of scales. The two sides of the equatio are equal, so the scales are balaced. If we add somethig to oe side of the

More information

Example 2 Find the square root of 0. The only square root of 0 is 0 (since 0 is not positive or negative, so those choices don t exist here).

Example 2 Find the square root of 0. The only square root of 0 is 0 (since 0 is not positive or negative, so those choices don t exist here). BEGINNING ALGEBRA Roots ad Radicals (revised summer, 00 Olso) Packet to Supplemet the Curret Textbook - Part Review of Square Roots & Irratioals (This portio ca be ay time before Part ad should mostly

More information

GCSE STATISTICS. 4) How to calculate the range: The difference between the biggest number and the smallest number.

GCSE STATISTICS. 4) How to calculate the range: The difference between the biggest number and the smallest number. GCSE STATISTICS You should kow: 1) How to draw a frequecy diagram: e.g. NUMBER TALLY FREQUENCY 1 3 5 ) How to draw a bar chart, a pictogram, ad a pie chart. 3) How to use averages: a) Mea - add up all

More information

Hypothesis testing. Null and alternative hypotheses

Hypothesis testing. Null and alternative hypotheses Hypothesis testig Aother importat use of samplig distributios is to test hypotheses about populatio parameters, e.g. mea, proportio, regressio coefficiets, etc. For example, it is possible to stipulate

More information

*The most important feature of MRP as compared with ordinary inventory control analysis is its time phasing feature.

*The most important feature of MRP as compared with ordinary inventory control analysis is its time phasing feature. Itegrated Productio ad Ivetory Cotrol System MRP ad MRP II Framework of Maufacturig System Ivetory cotrol, productio schedulig, capacity plaig ad fiacial ad busiess decisios i a productio system are iterrelated.

More information

Math C067 Sampling Distributions

Math C067 Sampling Distributions Math C067 Samplig Distributios Sample Mea ad Sample Proportio Richard Beigel Some time betwee April 16, 2007 ad April 16, 2007 Examples of Samplig A pollster may try to estimate the proportio of voters

More information

Chapter 7: Confidence Interval and Sample Size

Chapter 7: Confidence Interval and Sample Size Chapter 7: Cofidece Iterval ad Sample Size Learig Objectives Upo successful completio of Chapter 7, you will be able to: Fid the cofidece iterval for the mea, proportio, ad variace. Determie the miimum

More information

GOOD PRACTICE CHECKLIST FOR INTERPRETERS WORKING WITH DOMESTIC VIOLENCE SITUATIONS

GOOD PRACTICE CHECKLIST FOR INTERPRETERS WORKING WITH DOMESTIC VIOLENCE SITUATIONS GOOD PRACTICE CHECKLIST FOR INTERPRETERS WORKING WITH DOMESTIC VIOLENCE SITUATIONS I the sprig of 2008, Stadig Together agaist Domestic Violece carried out a piece of collaborative work o domestic violece

More information

THE ARITHMETIC OF INTEGERS. - multiplication, exponentiation, division, addition, and subtraction

THE ARITHMETIC OF INTEGERS. - multiplication, exponentiation, division, addition, and subtraction THE ARITHMETIC OF INTEGERS - multiplicatio, expoetiatio, divisio, additio, ad subtractio What to do ad what ot to do. THE INTEGERS Recall that a iteger is oe of the whole umbers, which may be either positive,

More information

Confidence Intervals for One Mean

Confidence Intervals for One Mean Chapter 420 Cofidece Itervals for Oe Mea Itroductio This routie calculates the sample size ecessary to achieve a specified distace from the mea to the cofidece limit(s) at a stated cofidece level for a

More information

A guide to School Employees' Well-Being

A guide to School Employees' Well-Being A guide to School Employees' Well-Beig Backgroud The public school systems i the Uited States employ more tha 6.7 millio people. This large workforce is charged with oe of the atio s critical tasks to

More information

Z-TEST / Z-STATISTIC: used to test hypotheses about. µ when the population standard deviation is unknown

Z-TEST / Z-STATISTIC: used to test hypotheses about. µ when the population standard deviation is unknown Z-TEST / Z-STATISTIC: used to test hypotheses about µ whe the populatio stadard deviatio is kow ad populatio distributio is ormal or sample size is large T-TEST / T-STATISTIC: used to test hypotheses about

More information

How to read A Mutual Fund shareholder report

How to read A Mutual Fund shareholder report Ivestor BulletI How to read A Mutual Fud shareholder report The SEC s Office of Ivestor Educatio ad Advocacy is issuig this Ivestor Bulleti to educate idividual ivestors about mutual fud shareholder reports.

More information

Present Value Factor To bring one dollar in the future back to present, one uses the Present Value Factor (PVF): Concept 9: Present Value

Present Value Factor To bring one dollar in the future back to present, one uses the Present Value Factor (PVF): Concept 9: Present Value Cocept 9: Preset Value Is the value of a dollar received today the same as received a year from today? A dollar today is worth more tha a dollar tomorrow because of iflatio, opportuity cost, ad risk Brigig

More information

The Importance of Media in the Classroom

The Importance of Media in the Classroom 01-TilestoVol09.qxd 8/25/03 3:47 PM Page 1 1 The Importace of Media i the Classroom As teachers, we have a wealth of iformatio from which to choose for our classrooms. We ca ow brig history ito the classroom

More information

1. C. The formula for the confidence interval for a population mean is: x t, which was

1. C. The formula for the confidence interval for a population mean is: x t, which was s 1. C. The formula for the cofidece iterval for a populatio mea is: x t, which was based o the sample Mea. So, x is guarateed to be i the iterval you form.. D. Use the rule : p-value

More information

Quadrat Sampling in Population Ecology

Quadrat Sampling in Population Ecology Quadrat Samplig i Populatio Ecology Backgroud Estimatig the abudace of orgaisms. Ecology is ofte referred to as the "study of distributio ad abudace". This beig true, we would ofte like to kow how may

More information

Time Value of Money. First some technical stuff. HP10B II users

Time Value of Money. First some technical stuff. HP10B II users Time Value of Moey Basis for the course Power of compoud iterest $3,600 each year ito a 401(k) pla yields $2,390,000 i 40 years First some techical stuff You will use your fiacial calculator i every sigle

More information

Research Method (I) --Knowledge on Sampling (Simple Random Sampling)

Research Method (I) --Knowledge on Sampling (Simple Random Sampling) Research Method (I) --Kowledge o Samplig (Simple Radom Samplig) 1. Itroductio to samplig 1.1 Defiitio of samplig Samplig ca be defied as selectig part of the elemets i a populatio. It results i the fact

More information

.04. This means $1000 is multiplied by 1.02 five times, once for each of the remaining sixmonth

.04. This means $1000 is multiplied by 1.02 five times, once for each of the remaining sixmonth Questio 1: What is a ordiary auity? Let s look at a ordiary auity that is certai ad simple. By this, we mea a auity over a fixed term whose paymet period matches the iterest coversio period. Additioally,

More information

Building Blocks Problem Related to Harmonic Series

Building Blocks Problem Related to Harmonic Series TMME, vol3, o, p.76 Buildig Blocks Problem Related to Harmoic Series Yutaka Nishiyama Osaka Uiversity of Ecoomics, Japa Abstract: I this discussio I give a eplaatio of the divergece ad covergece of ifiite

More information

OMG! Excessive Texting Tied to Risky Teen Behaviors

OMG! Excessive Texting Tied to Risky Teen Behaviors BUSIESS WEEK: EXECUTIVE EALT ovember 09, 2010 OMG! Excessive Textig Tied to Risky Tee Behaviors Kids who sed more tha 120 a day more likely to try drugs, alcohol ad sex, researchers fid TUESDAY, ov. 9

More information

Definition. A variable X that takes on values X 1, X 2, X 3,...X k with respective frequencies f 1, f 2, f 3,...f k has mean

Definition. A variable X that takes on values X 1, X 2, X 3,...X k with respective frequencies f 1, f 2, f 3,...f k has mean 1 Social Studies 201 October 13, 2004 Note: The examples i these otes may be differet tha used i class. However, the examples are similar ad the methods used are idetical to what was preseted i class.

More information

In nite Sequences. Dr. Philippe B. Laval Kennesaw State University. October 9, 2008

In nite Sequences. Dr. Philippe B. Laval Kennesaw State University. October 9, 2008 I ite Sequeces Dr. Philippe B. Laval Keesaw State Uiversity October 9, 2008 Abstract This had out is a itroductio to i ite sequeces. mai de itios ad presets some elemetary results. It gives the I ite Sequeces

More information

AGC s SUPERVISORY TRAINING PROGRAM

AGC s SUPERVISORY TRAINING PROGRAM AGC s SUPERVISORY TRAINING PROGRAM Learig Today...Leadig Tomorrow The Kowledge ad Skills Every Costructio Supervisor Must Have to be Effective The Associated Geeral Cotractors of America s Supervisory

More information

Simple Annuities Present Value.

Simple Annuities Present Value. Simple Auities Preset Value. OBJECTIVES (i) To uderstad the uderlyig priciple of a preset value auity. (ii) To use a CASIO CFX-9850GB PLUS to efficietly compute values associated with preset value auities.

More information

How to use what you OWN to reduce what you OWE

How to use what you OWN to reduce what you OWE How to use what you OWN to reduce what you OWE Maulife Oe A Overview Most Caadias maage their fiaces by doig two thigs: 1. Depositig their icome ad other short-term assets ito chequig ad savigs accouts.

More information

Discrete Mathematics and Probability Theory Spring 2014 Anant Sahai Note 13

Discrete Mathematics and Probability Theory Spring 2014 Anant Sahai Note 13 EECS 70 Discrete Mathematics ad Probability Theory Sprig 2014 Aat Sahai Note 13 Itroductio At this poit, we have see eough examples that it is worth just takig stock of our model of probability ad may

More information

CS100: Introduction to Computer Science

CS100: Introduction to Computer Science I-class Exercise: CS100: Itroductio to Computer Sciece What is a flip-flop? What are the properties of flip-flops? Draw a simple flip-flop circuit? Lecture 3: Data Storage -- Mass storage & represetig

More information

Chapter XIV: Fundamentals of Probability and Statistics *

Chapter XIV: Fundamentals of Probability and Statistics * Objectives Chapter XIV: Fudametals o Probability ad Statistics * Preset udametal cocepts o probability ad statistics Review measures o cetral tedecy ad dispersio Aalyze methods ad applicatios o descriptive

More information

Grade 7 Mathematics. Support Document for Teachers

Grade 7 Mathematics. Support Document for Teachers Grade 7 Mathematics Support Documet for Teachers G r a d e 7 M a t h e m a t i c s Support Documet for Teachers 2012 Maitoba Educatio Maitoba Educatio Cataloguig i Publicatio Data Grade 7 mathematics

More information

where: T = number of years of cash flow in investment's life n = the year in which the cash flow X n i = IRR = the internal rate of return

where: T = number of years of cash flow in investment's life n = the year in which the cash flow X n i = IRR = the internal rate of return EVALUATING ALTERNATIVE CAPITAL INVESTMENT PROGRAMS By Ke D. Duft, Extesio Ecoomist I the March 98 issue of this publicatio we reviewed the procedure by which a capital ivestmet project was assessed. The

More information

Confidence Intervals. CI for a population mean (σ is known and n > 30 or the variable is normally distributed in the.

Confidence Intervals. CI for a population mean (σ is known and n > 30 or the variable is normally distributed in the. Cofidece Itervals A cofidece iterval is a iterval whose purpose is to estimate a parameter (a umber that could, i theory, be calculated from the populatio, if measuremets were available for the whole populatio).

More information

Trigonometric Form of a Complex Number. The Complex Plane. axis. ( 2, 1) or 2 i FIGURE 6.44. The absolute value of the complex number z a bi is

Trigonometric Form of a Complex Number. The Complex Plane. axis. ( 2, 1) or 2 i FIGURE 6.44. The absolute value of the complex number z a bi is 0_0605.qxd /5/05 0:45 AM Page 470 470 Chapter 6 Additioal Topics i Trigoometry 6.5 Trigoometric Form of a Complex Number What you should lear Plot complex umbers i the complex plae ad fid absolute values

More information

Information for Programs Seeking Initial Accreditation

Information for Programs Seeking Initial Accreditation Iformatio for Programs Seekig Iitial Accreditatio Aswers to Frequetly- Asked-Questios (from www.abet.org/ew-to-accreditatio/) Assurig Quality l Stimulatig Iovatio This documet iteds to aswer may of the

More information

2-3 The Remainder and Factor Theorems

2-3 The Remainder and Factor Theorems - The Remaider ad Factor Theorems Factor each polyomial completely usig the give factor ad log divisio 1 x + x x 60; x + So, x + x x 60 = (x + )(x x 15) Factorig the quadratic expressio yields x + x x

More information

IMPROVING AWARENESS ABOUT THE MEANING OF THE PRINCIPLE OF MATHEMATICAL INDUCTION

IMPROVING AWARENESS ABOUT THE MEANING OF THE PRINCIPLE OF MATHEMATICAL INDUCTION IMPROVING AWARENESS ABOUT THE MEANING OF THE PRINCIPLE OF MATHEMATICAL INDUCTION Aalisa Cusi ad Nicolia A. Malara This work is based o our covictio that it is possible to miimize difficulties studets face

More information

Introducing Your New Wells Fargo Trust and Investment Statement. Your Account Information Simply Stated.

Introducing Your New Wells Fargo Trust and Investment Statement. Your Account Information Simply Stated. Itroducig Your New Wells Fargo Trust ad Ivestmet Statemet. Your Accout Iformatio Simply Stated. We are pleased to itroduce your ew easy-to-read statemet. It provides a overview of your accout ad a complete

More information

Setting Up a Contract Action Network

Setting Up a Contract Action Network CONTRACT ACTION NETWORK Settig Up a Cotract Actio Network This is a guide for local uio reps who wat to set up a iteral actio etwork i their worksites. This etwork cosists of: The local uio represetative,

More information

One-sample test of proportions

One-sample test of proportions Oe-sample test of proportios The Settig: Idividuals i some populatio ca be classified ito oe of two categories. You wat to make iferece about the proportio i each category, so you draw a sample. Examples:

More information

The Big Picture: An Introduction to Data Warehousing

The Big Picture: An Introduction to Data Warehousing Chapter 1 The Big Picture: A Itroductio to Data Warehousig Itroductio I 1977, Jimmy Carter was Presidet of the Uited States, Star Wars hit the big scree, ad Apple Computer, Ic. itroduced the world to the

More information

Center, Spread, and Shape in Inference: Claims, Caveats, and Insights

Center, Spread, and Shape in Inference: Claims, Caveats, and Insights Ceter, Spread, ad Shape i Iferece: Claims, Caveats, ad Isights Dr. Nacy Pfeig (Uiversity of Pittsburgh) AMATYC November 2008 Prelimiary Activities 1. I would like to produce a iterval estimate for the

More information

STUDENTS PARTICIPATION IN ONLINE LEARNING IN BUSINESS COURSES AT UNIVERSITAS TERBUKA, INDONESIA. Maya Maria, Universitas Terbuka, Indonesia

STUDENTS PARTICIPATION IN ONLINE LEARNING IN BUSINESS COURSES AT UNIVERSITAS TERBUKA, INDONESIA. Maya Maria, Universitas Terbuka, Indonesia STUDENTS PARTICIPATION IN ONLINE LEARNING IN BUSINESS COURSES AT UNIVERSITAS TERBUKA, INDONESIA Maya Maria, Uiversitas Terbuka, Idoesia Co-author: Amiuddi Zuhairi, Uiversitas Terbuka, Idoesia Kuria Edah

More information

Page 1. Real Options for Engineering Systems. What are we up to? Today s agenda. J1: Real Options for Engineering Systems. Richard de Neufville

Page 1. Real Options for Engineering Systems. What are we up to? Today s agenda. J1: Real Options for Engineering Systems. Richard de Neufville Real Optios for Egieerig Systems J: Real Optios for Egieerig Systems By (MIT) Stefa Scholtes (CU) Course website: http://msl.mit.edu/cmi/ardet_2002 Stefa Scholtes Judge Istitute of Maagemet, CU Slide What

More information

Determining the sample size

Determining the sample size Determiig the sample size Oe of the most commo questios ay statisticia gets asked is How large a sample size do I eed? Researchers are ofte surprised to fid out that the aswer depeds o a umber of factors

More information

hands-on mathematics Geometry, Mental Math, Measurement, Number Concepts, Number Operations, Patterns and Relations, Statistics and Probability

hands-on mathematics Geometry, Mental Math, Measurement, Number Concepts, Number Operations, Patterns and Relations, Statistics and Probability 4 hads-o mathematics Geometry, Metal Math, Measuremet, Number Cocepts, Number Operatios, Patters ad Relatios, Statistics ad Probability Program Implemetatio Program Resources Hads-O Mathematics is arraged

More information

supply-chain management (scm)

supply-chain management (scm) 7 supply-chai maagemet (scm) 7. 1 Theory: SCM ad logistics Learig outcomes Lear about the theory of supply-chai maagemet. Use collocatios coected with supply-chai maagemet. Desig a supply chai. Itroductio

More information

CHAPTER 11 Financial mathematics

CHAPTER 11 Financial mathematics CHAPTER 11 Fiacial mathematics I this chapter you will: Calculate iterest usig the simple iterest formula ( ) Use the simple iterest formula to calculate the pricipal (P) Use the simple iterest formula

More information

Solving Logarithms and Exponential Equations

Solving Logarithms and Exponential Equations Solvig Logarithms ad Epoetial Equatios Logarithmic Equatios There are two major ideas required whe solvig Logarithmic Equatios. The first is the Defiitio of a Logarithm. You may recall from a earlier topic:

More information

LIFE CYCLES UNIT OVERVIEW THE BIG IDEA. Other topics SPARK. Materials. Activity

LIFE CYCLES UNIT OVERVIEW THE BIG IDEA. Other topics SPARK. Materials. Activity LIFE CYCLES UNIT OVERVIEW All livig thigs go through chages as they grow ad develop. Although idividual orgaisms die, ew oes replace them, which esures the survival of the species. Durig its life cycle,

More information

NATIONAL SENIOR CERTIFICATE GRADE 12

NATIONAL SENIOR CERTIFICATE GRADE 12 NATIONAL SENIOR CERTIFICATE GRADE MATHEMATICS P EXEMPLAR 04 MARKS: 50 TIME: 3 hours This questio paper cosists of 8 pages ad iformatio sheet. Please tur over Mathematics/P DBE/04 NSC Grade Eemplar INSTRUCTIONS

More information

15.075 Exam 3. Instructor: Cynthia Rudin TA: Dimitrios Bisias. November 22, 2011

15.075 Exam 3. Instructor: Cynthia Rudin TA: Dimitrios Bisias. November 22, 2011 15.075 Exam 3 Istructor: Cythia Rudi TA: Dimitrios Bisias November 22, 2011 Gradig is based o demostratio of coceptual uderstadig, so you eed to show all of your work. Problem 1 A compay makes high-defiitio

More information

INDEPENDENT BUSINESS PLAN EVENT 2016

INDEPENDENT BUSINESS PLAN EVENT 2016 INDEPENDENT BUSINESS PLAN EVENT 2016 The Idepedet Busiess Pla Evet ivolves the developmet of a comprehesive proposal to start a ew busiess. Ay type of busiess may be used. The Idepedet Busiess Pla Evet

More information

Case Study. Normal and t Distributions. Density Plot. Normal Distributions

Case Study. Normal and t Distributions. Density Plot. Normal Distributions Case Study Normal ad t Distributios Bret Halo ad Bret Larget Departmet of Statistics Uiversity of Wiscosi Madiso October 11 13, 2011 Case Study Body temperature varies withi idividuals over time (it ca

More information

Pre-Suit Collection Strategies

Pre-Suit Collection Strategies Pre-Suit Collectio Strategies Writte by Charles PT Phoeix How to Decide Whether to Pursue Collectio Calculatig the Value of Collectio As with ay busiess litigatio, all factors associated with the process

More information

Here are a couple of warnings to my students who may be here to get a copy of what happened on a day that you missed.

Here are a couple of warnings to my students who may be here to get a copy of what happened on a day that you missed. This documet was writte ad copyrighted by Paul Dawkis. Use of this documet ad its olie versio is govered by the Terms ad Coditios of Use located at http://tutorial.math.lamar.edu/terms.asp. The olie versio

More information

6. p o s I T I v e r e I n f o r c e M e n T

6. p o s I T I v e r e I n f o r c e M e n T 6. p o s I T I v e r e I f o r c e M e T The way positive reiforcemet is carried out is more importat tha the amout. B.F. Skier We all eed positive reiforcemet. Whether or ot we are cosciously aware of

More information

Domain 1: Designing a SQL Server Instance and a Database Solution

Domain 1: Designing a SQL Server Instance and a Database Solution Maual SQL Server 2008 Desig, Optimize ad Maitai (70-450) 1-800-418-6789 Domai 1: Desigig a SQL Server Istace ad a Database Solutio Desigig for CPU, Memory ad Storage Capacity Requiremets Whe desigig a

More information

Non-life insurance mathematics. Nils F. Haavardsson, University of Oslo and DNB Skadeforsikring

Non-life insurance mathematics. Nils F. Haavardsson, University of Oslo and DNB Skadeforsikring No-life isurace mathematics Nils F. Haavardsso, Uiversity of Oslo ad DNB Skadeforsikrig Mai issues so far Why does isurace work? How is risk premium defied ad why is it importat? How ca claim frequecy

More information

SEQUENCES AND SERIES

SEQUENCES AND SERIES Chapter 9 SEQUENCES AND SERIES Natural umbers are the product of huma spirit. DEDEKIND 9.1 Itroductio I mathematics, the word, sequece is used i much the same way as it is i ordiary Eglish. Whe we say

More information

Predictive Modeling Data. in the ACT Electronic Student Record

Predictive Modeling Data. in the ACT Electronic Student Record Predictive Modelig Data i the ACT Electroic Studet Record overview Predictive Modelig Data Added to the ACT Electroic Studet Record With the release of studet records i September 2012, predictive modelig

More information