Predictive Modeling Data. in the ACT Electronic Student Record


 Stewart Hall
 3 years ago
 Views:
Transcription
1 Predictive Modelig Data i the ACT Electroic Studet Record
2 overview Predictive Modelig Data Added to the ACT Electroic Studet Record With the release of studet records i September 2012, predictive modelig data from four behavioral idexes were added to the ACT Electroic Studet Record. A fifth idex, the Iterest Major Fit score, was also added to the studet record. The predictive modelig data elemets do ot predict whether a studet will eroll at a specific istitutio; rather, they predict four erollmet behaviors: The Mobility Idex predicts the likelihood of a studet erollig out of state. Mobility Idex scores rage from a low of 01 to a high of 99. The higher the score, the more likely the studet will eroll out of state. The Istitutio Type Idex predicts the likelihood of a studet erollig at a private college or uiversity. Istitutio Type Idex scores rage from a low of 01 to a high of 99. The higher the score, the more likely the studet will eroll at a private istitutio. The Selectivity Idex predicts the selectivity of the istitutio at which a studet is most likely to eroll. Selectivity Idex scores rage from 0.0 to 5.0, i icremets of 0.1. A higher Selectivity Idex correspods to a greater likelihood of attedig a more selective istitutio. The Istitutio Size Idex predicts the size of the istitutio at which the studet is most likely to eroll. Istitutio Size Idex scores rage from 0.0 to 4.0, i icremets of 0.1. A higher Istitutio Size Idex score correspods to a greater likelihood of attedig a larger istitutio. Percetile raks for each idex are ot reported o the studet score report; crosswalk tables showig percetile raks for all idex values will be available o the ACT website at 2
3 idex locatios Positio Locatios of the Idexes i the ACT Electroic Studet Record I Table 1 below are the positios of the five ew idexes i the ACT Electroic Studet Record: Table 1. Five ew idexes i the ACT Electroic Studet Record Field Positio Rage Mobility Idex Istitutio Type Idex Selectivity Idex with a implied decimal Istitutio Size Idex with a implied decimal Iterest Major Fit Score All data are umeric. If o predictios are made, the fields are set to blak. The full record layout is available olie at accuracy Accuracy of the Predictive Modelig Fields The four Predictive Modelig Idexes are built usig actual data o erolled studets ad are, therefore, very accurate. Every year, ACT seds a file of all ACTtested studets i that year s high school graduatig class to the Natioal Studet Clearighouse, which matches the ACT file with its istitutioal erollmet records. More tha 3,300 colleges ad uiversities, erollig more tha 96% of all studets i public ad private U.S. istitutios, participate i the Clearighouse. As a result, ACT has iformatio o firsttime college erollmets for most of the ACTtested studets who go to college. Because ACT has data o which studets go out of state, which studets atted private istitutios, ad the selectivity ad size of the istitutios that studets atted, it is possible to develop predictive models that accurately predict those four behaviors. The accuracy of the Predictive Modelig Idexes relates directly to the selfreported iformatio provided by studets about their erollmet prefereces (type ad size of istitutio ad preferred distace from home to campus), as well as the types of istitutios they are cosiderig. Whe studets take the ACT, they ca choose to sed their scores to up to six istitutios i preferece order. ACT calls this mix of campuses (istate/out of state, public/private, selective/oselective, large/small) the Choice Set. Through extesive research, ACT has determied that this Choice Set carries a predictive power that far outweighs other variables. Because the idexes reflect a studet s Choice Set i measurable ad objective ways, they provide quite a bit of iformatio ot oly o studet erollmet itetios but also o your competitio the other istitutios besides yours to which a studet seds scores. 3
4 mobility idex Predictig OutofState Erollmet The Mobility Idex predicts the likelihood that a studet will eroll at a outofstate istitutio, usig (1) the studet Choice Set or campus mix (campuses to which studets sed scores); (2) ACT Composite score; (3) preferred distace from home to campus; ad (4) selected other variables. The campus mix comprises more tha fourfifths of the Mobility Idex model; this suggests that the studet score seder choices are itetioal, ad thus serve as the basis for accurate predictios. If studets sed scores to outofstate istitutios, they are much more likely to eroll out of state. If studets do ot sed scores to outofstate istitutios, they are very ulikely to eroll out of state. The score rage of the Mobility Idex is 01 to 99. The higher the score, the more likely the studet is to eroll out of state. The lower the score, the more likely the studet is to eroll istate. Table 2 shows the percet of studets i the 2011 ACTtested graduatig class whose Mobility Idex score falls withi specific rages. ACT research shows that oly about 20% of ACTtested studets eroll out of state, so most Mobility Idex scores are quite low; 53% of all ACTtested studets have Mobility Idex scores of 20 or lower. Studets with Mobility Idex scores of 20 or higher are more likely tha average to eroll out of state. Studets with very low scores o the Mobility Idex are lookig oly istate. Studets with very high scores are probably lookig oly out of state. Studets i the middle rages are probably lookig both istate ad out of state. Table 3 below shows the percet of studets i the 2011 ACTtested graduatig class by ACT Composite score ad for a give rage of Mobility Idex scores. Studets with a Composite score below 24 ted to have lower Mobility Idex scores. Studets with a Composite score of 24 ad above ted to have higher Mobility Idex scores. Studets with ACT Composite scores of 24 ad higher are more likely tha average to go out of state. Table 3. Percet of 2011 ACTtested graduates at combiatios of ACT Composite score rage ad Mobility Idex score rage Table 2. Percet of 2011 ACTtested graduates i each Mobility Idex score rage Mobility Idex Score Rages Mobility Idex ACT Composite Score Rages Score Rages Percet i Each Rage (Natioal)
5 istitutio type idex Predictig Erollmet at a Private College or Uiversity The Istitutio Type Idex predicts the likelihood that a studet will eroll at a private college or uiversity by usig (1) the studet Choice Set or campus mix (campuses to which a studet seds scores); (2) ACT Composite score; (3) preferred istitutio type; ad (4) selected other variables. The campus mix comprises almost twothirds of the Istitutio Type Idex model. Preferred istitutio type accouts for aother quarter of the model. Together, the campus mix ad preferred istitutio type accout for almost 90% of the model; this suggests that the studet score seder choices are itetioal, ad thus serve as the basis for accurate predictios. The score rage of the Istitutio Type Idex is 01 to 99. The higher the score, the more likely the studet is to eroll at a private college or uiversity. The lower the score, the more likely the studet is to eroll at a public college or uiversity. Table 4 shows the percet of studets i the 2011 ACTtested graduatig class whose Istitutio Type Idex scores fall withi specific rages. ACT research shows that oly about 20% of ACTtested studets eroll at private istitutios, so most Istitutio Type Idex scores are quite low; 61% of all ACTtested studets have Istitutio Type Idex scores of 20 or lower. Studets with Istitutio Type Idex scores of 20 or higher are more likely tha average to eroll at private istitutios. Studets with very low scores o the Istitutio Type Idex are probably lookig oly at public istitutios. Studets with very high scores are probably lookig oly at private istitutios. Studets i the middle rages are probably lookig at both public ad private istitutios. Table 5 below shows the percet of studets i the 2011 ACTtested graduatig class by ACT Composite score ad for a give rage of Istitutio Type Idex scores. Studets with a Composite score below 24 ted to have lower Istitutio Type scores. Studets with a Composite score of 24 ad above ted to have higher Istitutio Type Idex scores. Table 5. Percet of 2011 ACTtested graduates i combiatios of ACT Composite score rage ad Istitutio Type Idex score rage Table 4. Percet of 2011 ACTtested graduates i each Istitutio Type Idex score rage Istitutio Type Idex Score Rages Istitutio Type ACT Composite Score Rages Idex Score Rages Percet i Each Rage (Natioal)
6 selectivity idex Predictig Erollmet by Selectivity of Istitutio ACT classifies colleges ad uiversities ito five categories of admissio selectivity: highly selective, selective, traditioal, liberal, ad ope. The Selectivity Idex predicts the selectivity of the istitutio at which the studet is likely to eroll, usig (1) the studet Choice Set or campus mix (campuses to which a studet seds scores); (2) ACT Composite score; (3) high school GPA; ad (4) selected other variables. The campus mix comprises more tha threefourths of the Selectivity Idex model. ACT Composite score accouts for aother 14% of the model. Together, the campus mix ad ACT Composite score accout for 93% of the model; this suggests that the studet score seder choices are itetioal, ad thus serve as the basis for accurate predictios. Studets are very likely to sed scores to istitutios that are similar i selectivity to the istitutio at which they will evetually eroll. The score rage of the Selectivity Idex is 0.0 to 5.0. The higher the score, the more likely the studet is to atted a more selective college or uiversity. The lower the score, the more likely the studet is to atted a less selective college or uiversity. Table 6 shows the percet of studets i the 2011 ACTtested graduatig class whose Selectivity Idex scores fall withi specific rages. Threefourths of all of these studets eroll at istitutios i the Traditioal ad Selective categories. Istitutios which are highly selective or which are ope admissio will see most erolled studets distributed at the top or bottom of the Selectivity Idex scale. Istitutios of medium or traditioal Traditioal Selective Highly Selective selectivity ofte fid that they eroll sigificat umbers of studets from several selectivity bads. This, agai, may reflect a populatio lookig at several types ad sizes of istitutios close to home. Table 7 below shows the percet of studets i the 2011 ACTtested graduatig class by ACT Composite score ad for a give rage of Selectivity Idex scores. Studets with a Composite score below 24 ted to have lower Selectivity Idex scores. Studets with a Composite score of 24 ad above have much higher Selectivity Idex scores. Table 7. Percet of 2011 ACTtested graduates at combiatios of ACT Composite score rage ad Selectivity Idex score rage Selectivity Idex Score Rages Selectivity Category Table 6. Percet of 2011 ACTtested graduates i each Selectivity Idex score rage Selectivity Idex Score Rages Percet i Each Rage (Natioal) Selectivity Category Ope Liberal ACT Composite Score Rages Ope Liberal Traditioal Selective Highly Selective
7 istitutio size idex Predictig Erollmet by Size of Istitutio For purposes of the Istitutio Size Idex, ACT classifies colleges ad uiversities ito four size categories of fulltime udergraduate populatios: fewer tha 5,000, 5,000 9,999, 10,000 19,999, ad more tha 20,000. The Istitutio Size Idex predicts the size of the istitutio at which the studet is likely to eroll, usig (1) the studet Choice Set or campus mix (campuses to which a studet seds scores); (2) ACT Composite score; (3) preferred college size; ad (4) selected other variables. The campus mix comprises more tha fourfifths of the Istitutio Size Idex model. ACT Composite score accouts for aother 7% of the model. Together, the campus mix ad ACT Composite score accout for more tha 95% of the model; this suggests that the studet score seder choices are itetioal, ad thus serve as the basis for accurate predictios. Studets are very likely to sed scores to istitutios that are similar i size to the istitutio at which they will evetually eroll. The score rage of the Istitutio Size Idex is.01 to 4.0. The higher the score, the more likely the studet is to atted a larger istitutio. The lower the score, the more likely the studet is to atted a smaller istitutio. Table 8 shows the percet of studets i the 2011 ACTtested graduatig class whose Istitutio Size Idex scores fall withi specific rages; 64% of studets eroll at istitutios i the medium ad large categories. Table 8. Percet of 2011 ACTtested graduates i each Istitutio Size Idex score rage Istitutio Size Idex Score Rages Percet i Each Rage (Natioal) Istitutio Size Categories Istitutio Size Type <5,000 Small ,000 9,999 Medium ,000 19,999 Large >20,000 Very Large If your istitutio is very large or very small, you will see most erolled studets distributed at the top or bottom of the Istitutio Size Idex score rages. If your istitutio is medium size, you may fid that you eroll sigificat umbers of studets from several size bads. This, agai, may reflect a populatio lookig at several types ad sizes of istitutios close to home. Table 9 below shows the percet of studets i the 2011 ACTtested graduatig class by ACT Composite score ad for a give rage of Istitutio Size Idex scores. Studets with a Composite score below 20 ted to have lower Istitutio Size Idex scores. Studets with a Composite Score of 24 ad above have much higher Size Idex scores. Studets with a Composite score of are evely distributed across all size categories. Table 9. Percet of 2011 ACTtested graduates at combiatios of ACT Composite score rage ad Istitutio Size Idex score rage Istitutio Size Istitutio Size ACT Composite Score Rages Idex Score Rages Categories <5, ,000 9, ,000 19, >20,
8 relatioships The Relatioship of ACT Composite Score, Distace from Home to Campus, ad the Idexes ACT research suggests that academic ability is the primary determier of erollmet behavior. As ability levels rise, studets are more likely to eroll out of state, more likely to eroll at a private college or uiversity, more likely to eroll at a selective istitutio, ad more likely to eroll at a larger istitutio. Table 10 below shows the media miles from home to campus for erolled studets i the 2011 ACTtested graduatig class by selectivity. Miles are provided at the 25th percetile, the media, ad the 75th percetile. Table 10. Miles from home to campus for 2011 ACTtested graduates by admissio policy Admissio Policy Cout 25th Percetile Media 75th Percetile Highly Selective 111, Selective 267, Traditioal 308, Liberal 29, Ope 310, Missig 115, Because the idexes are determied so much by the Choice Set, it is importat to cosider how the Choice Set is iflueced by ACT Composite score as well as distace from home to campus. For may studets, the primary erollmet itetio is to eroll close to home. I fact, about half of all ACTtested studets eroll withi 50 miles of home. Whe cosiderig colleges close to home, it is ot ucommo for a studet to cosider a wide variety of istitutios: 2year/4year, public/private, ad istitutios of varyig size ad selectivity. I cotrast, studets plaig to eroll farther from home or out of state ted to sed scores to istitutios that match their specific erollmet itetios. For example, a studet might look at several selective private istitutios out of state or several large public istitutios out of state. O the other had, it would be very uusual for a studet to sed scores outofstate to a 2year school ad a 4year school or to a selective public school ad a oselective public school. 8
9 data sheets Predictive Modelig Istitutio Data Sheets To use the idex data strategically, istitutios will wat to kow how predictive the idexes are for erolled studets at their istitutio. For most istitutios i the coutry, ACT is able to prepare a Predictive Modelig Istitutio Data Sheet that shows a istitutio s 2011 ACTtested erolled studets i terms of the four idexes. To request a Predictive Modelig Istitutio Data Sheet for your istitutio, cotact or your ACT regioal represetative. Here are a few geeral rules for iterpretig the data sheets: If the erolled percetages at your istitutio match closely with the atioal distributios for each idex, this likely meas that your istitutio is less selective ad that most of your erollmet comes from studets who live fairly close to your campus. The takeaway i this case is that may studets may be choosig your istitutio because of where your istitutio is located rather tha because of what your istitutio is (2year/4year, public/private, selective/oselective, large/small). Studets may be choosig your istitutio because it is close to where they live, ad oe of their primary erollmet itetios may be to eroll close to home. I cotrast, more selective istitutios will usually show erolled studet distributios quite differet from the atioal distributios because they are erollig more studets from a greater distace ad those who are lookig for a specific size, type, ad selectivity of istitutio. 9
10 strategic use Usig the Idex Data Strategically Studet score reports set to colleges ad uiversities cotai more tha 265 data fields that ca be used for two strategic purposes: (1) assessig a studet s postsecodary erollmet itetios; ad (2) assessig a studet s level of iterest i your istitutio. Iformatio about studet erollmet itetios comes from the four idexes, studet erollmet prefereces for size ad type of istitutio ad preferred distace from home to campus, ad other variables such as highest degree expected. Iformatio about a studet s level of iterest i your istitutio comes from level of college choice (or where they sed their scores) ad how closely their iterests, eeds, ad prefereces match your istitutio. Iformatio i the score report o studet iterests, plas, ad eeds ca be used to persoalize commuicatios. Like other data i the ACT score report, the four Predictive Modelig Idexes provide you with data which ca be used to better target, segmet, ad commuicate with studets. The idexes represet poititime erollmet itetios, ad they should always be viewed as startig poits. Ay subsequet iteractios that a istitutio has with a studet will icrease or decrease that studet s likelihood of erollig. However, as a startig poit, the iformatio from the idexes ca be used to determie how well a studet s erollmet itetios match the characteristics of your istitutio. A coveiet way to thik about segmetig is to assig every score seder to oe of four quadrats, as show i Figure 1 below. The vertical axis represets how desirable a studet is to your istitutio; the horizotal axis represets how well the studet s erollmet itetios (as measured by the idexes ad level of college choice) match your istitutio. Your istitutio will have target populatios that are very desirable ad a good match (upper left quadrat). O the other had, your istitutio may have target populatios that are very desirable but ot a good match at least whe usig the idexes as a startig poit to describe studet erollmet itetios ad these fall i the lower left quadrat. I that example, you ca use the idex scores to uderstad studet erollmet itetios ad the build strategies to move studets toward a better uderstadig of what your istitutio offers. Other studets may fall i the quadrat of low desirability ad poor match, ad you may choose to sped less time ad fewer resources recruitig those studets. Figure 1. Quadrats of studet desirability as matched to academic itetios More Desirable Good Match More Desirable Poor Match Less Desirable Good Match Less Desirable Poor Match 10
11 predictive modelig ad EOS The Predictive Modelig Idexes ad Studet Search through ACT EOS For the past three years, data from the four Predictive Modelig Idexes is icluded with every ACT Educatioal Opportuity Service (EOS) record that is dowloaded. EOS cliets caot use the idexes as search variables i ACT EOS, but each idex is closely related to a data variable that ca be used whe orderig. For example, ACT research shows that studets who say that wat to eroll withi 25 miles of home have very low Mobility Idex scores. As a result, ACT recommeds that istitutios do ot select the ames of studets i ACT EOS who wat to eroll withi 25 miles of home whe selectig the ames of outofstate studets who live more tha 100 miles from the campus. Other strategies for usig the idex data strategically i ACT EOS are summarized i Usig EOS Search Criteria Related to Predictive Modelig Idexes.pdf, available olie at Table 11 below shows the positios of the four idexes (ad the associated percetile raks) i the ACT EOS Electroic Studet Record: Table 11. Positios of Predictive Modelig Idexes i the ACT EOS Electroic Studet Record Field Positio/Format Rage Mobility Idex (x.xx) Percetile Rak Mobility Idex (xx) Istitutio Type Idex (x.xx) Percetile Rak Istitutio Type Idex (xx) Selectivity Idex (x.x) Percetile Rak Selectivity Idex (xx) Istitutio Size Idex (x.x) Percetile Rak Istitutio Size Idex (xx) 11
12 fit scores Iterest Major Fit Score Effective with the release of studet records i September 2012, a Iterest Major Fit score was added to the ACT electroic record. Iterestmajor fit is derived from two data elemets that are collected durig ACT registratio: (1) the studet s ACT Iterest Ivetory scores; ad (2) the studet s iteded major chose from a list of 294 college majors. The Iterest Major Fit score measures the stregth of the relatioship betwee the studet s ACT Iterest Ivetory scores ad the profile of iterests of studets i a give major. IterestMajor Fit scores rage from a low of 01 to a high of 99. The higher the score, the better the iterest major fit. Research at ACT ad elsewhere suggests that if studets measured iterests are similar to the iterests of people i their chose college majors, they will be more likely to: persist i college remai i their major complete their college degree i a timely maer Iterest major fit clearly beefits both studets ad the college they atted: studets egaged i goodfit majors are more likely to stay i college, stay i their major, ad fiish sooer. Iterest profiles for majors are based o a atioal sample of udergraduate studets with a declared major ad a GPA of at least 2.0. Major was determied i the third year for studets i 4year colleges, ad i the secod year for studets i 2year colleges. 12
13 strategic use Recruitmet Strategies for Usig the Iterest Major Fit Score May istitutios build mail, , ad telephoe outreach strategies tied to studet iteded major. For example, these strategies may iclude mailig or ig iformatio about academic departmets ad colleges, or, i some cases, havig professors or studets cotact prospective studet majors by phoe. All such outreach strategies cost time ad moey. By combiig data from a studet s iteded major with a Iterest Major Fit score, campuses ca idetify studets who may have a stroger iterest i a particular major ad who may be more likely to eroll i a particular major. As a result, campuses could do more targeted outreach usig both iteded academic major ad iterest major fit ad, perhaps, save time, moey, ad resources. Advisig ad Retetio Strategies for Usig the Iterest Major Fit Score College retetio itervetios ofte iclude efforts to idetify studets more likely to drop out or ot make adequate progress toward a degree. Because ACT research suggests that good iterest major fit ca lead to positive retetio ad persistece outcomes, it follows that advisors ad retetio professioals ca use poor IterestMajor Fit scores as a part of efforts to idetify atrisk studets or studets who could beefit from advisig ad career plaig itervetios by ACT, Ic. All rights reserved. The ACT is a registered trademark of ACT, Ic., i the U.S.A. ad other coutries
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 informationHypothesis 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 informationConfidence 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 informationThe 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 informationDetermining 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 informationIn 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 informationImpact your future. Make plans with good advice from ACT. Get Set for College 1. THINK 2. CONSIDER 3. COMPARE 4. APPLY 5. PLAN 6.
Impact your future Get Set for College 1. THINK 2. CONSIDER 3. COMPARE 4. APPLY 5. PLAN 6. DECIDE Make plas with good advice from ACT. 1. Thik Thik about yourself ad your college eeds Do you start thigs
More informationYour organization has a Class B IP address of 166.144.0.0 Before you implement subnetting, the Network ID and Host ID are divided as follows:
Subettig Subettig is used to subdivide a sigle class of etwork i to multiple smaller etworks. Example: Your orgaizatio has a Class B IP address of 166.144.0.0 Before you implemet subettig, the Network
More informationMath 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 informationHow 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 shortterm assets ito chequig ad savigs accouts.
More informationOne Goal. 18Months. Unlimited Opportunities.
18 fasttrack 18Moth BACHELOR S DEGREE completio PROGRAMS Oe Goal. 18Moths. Ulimited Opportuities. www.ortheaster.edu/cps FastTrack Your Bachelor s Degree ad Career Goals Complete your bachelor s degree
More informationProfessional 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 informationMaking training work for your business
Makig traiig work for your busiess Itegratig core skills of laguage, literacy ad umeracy ito geeral workplace traiig makes sese. The iformatio i this pamphlet will help you pla for ad build a successful
More informationGCSE 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 informationHypergeometric Distributions
7.4 Hypergeometric Distributios Whe choosig the startig lieup 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 informationAP Calculus BC 2003 Scoring Guidelines Form B
AP Calculus BC Scorig Guidelies Form B The materials icluded i these files are iteded for use by AP teachers for course ad exam preparatio; permissio for ay other use must be sought from the Advaced Placemet
More information1. 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 : pvalue
More informationZTEST / ZSTATISTIC: used to test hypotheses about. µ when the population standard deviation is unknown
ZTEST / ZSTATISTIC: used to test hypotheses about µ whe the populatio stadard deviatio is kow ad populatio distributio is ormal or sample size is large TTEST / TSTATISTIC: used to test hypotheses about
More informationUniversity of California, Los Angeles Department of Statistics. Distributions related to the normal distribution
Uiversity of Califoria, Los Ageles Departmet of Statistics Statistics 100B Istructor: Nicolas Christou Three importat distributios: Distributios related to the ormal distributio Chisquare (χ ) distributio.
More informationCS103X: 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 sixfigure salaries i whole dollar amouts are there that cotai at least
More informationMeasures 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 informationinsight 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 datadrive decisios.
More informationGOOD 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 informationA guide to School Employees' WellBeing
A guide to School Employees' WellBeig 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 informationConfidence 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 informationFor customers Key features of the Guaranteed Pension Annuity
For customers Key features of the Guarateed Pesio Auity The Fiacial Coduct Authority is a fiacial services regulator. It requires us, Aego, to give you this importat iformatio to help you to decide whether
More informationHow to set up your GMC Online account
How to set up your GMC Olie accout Mai title Itroductio GMC Olie is a secure part of our website that allows you to maage your registratio with us. Over 100,000 doctors already use GMC Olie. We wat every
More informationInformation for Programs Seeking Initial Accreditation
Iformatio for Programs Seekig Iitial Accreditatio Aswers to Frequetly AskedQuestios (from www.abet.org/ewtoaccreditatio/) Assurig Quality l Stimulatig Iovatio This documet iteds to aswer may of the
More informationConfidence Intervals
Cofidece Itervals Cofidece Itervals are a extesio of the cocept of Margi of Error which we met earlier i this course. Remember we saw: The sample proportio will differ from the populatio proportio by more
More informationMEP Pupil Text 9. The mean, median and mode are three different ways of describing the average.
9 Data Aalysis 9. Mea, Media, Mode ad Rage I Uit 8, you were lookig at ways of collectig ad represetig data. I this uit, you will go oe step further ad fid out how to calculate statistical quatities which
More informationFlood Emergency Response Plan
Flood Emergecy Respose Pla This reprit is made available for iformatioal purposes oly i support of the isurace relatioship betwee FM Global ad its cliets. This iformatio does ot chage or supplemet policy
More informationHow Direct Mail Has Evolved into Smart Mail More Powerful. More Profitable. Whitepaper
Whitepaper Paper iteded for: AUDIENCE: Those resposible for atioal or local bradig, ad chael marketig. FOCUS: How mail marketig has evolved, what it delivers, how it ca be measured, the ROI ad advatages
More informationTIAACREF Wealth Management. Personalized, objective financial advice for every stage of life
TIAACREF Wealth Maagemet Persoalized, objective fiacial advice for every stage of life A persoalized team approach for a trusted lifelog relatioship No matter who you are, you ca t be a expert i all aspects
More informationDC College Savings Plan Helping Children Reach a Higher Potential
529 DC College Savigs Pla Helpig Childre Reach a Higher Potetial reach Sposored by Govermet of the District of Columbia Office of the Mayor Office of the Chief Fiacial Officer Office of Fiace ad Treasury
More informationProperties of MLE: consistency, asymptotic normality. Fisher information.
Lecture 3 Properties of MLE: cosistecy, asymptotic ormality. Fisher iformatio. I this sectio we will try to uderstad why MLEs are good. Let us recall two facts from probability that we be used ofte throughout
More informationMultiple Representations for Pattern Exploration with the Graphing Calculator and Manipulatives
Douglas A. Lapp Multiple Represetatios for Patter Exploratio with the Graphig Calculator ad Maipulatives To teach mathematics as a coected system of cocepts, we must have a shift i emphasis from a curriculum
More informationMathematical 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 informationInference 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 informationKen blanchard college of business
Ke blachard College of BUSINESS a history of excellece Established i 1949, Grad Cayo Uiversity has more tha a 60year track record of helpig studets achieve their academic goals. The Ke Blachard College
More information5.4 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 informationPENSION ANNUITY. Policy Conditions Document reference: PPAS1(7) This is an important document. Please keep it in a safe place.
PENSION ANNUITY Policy Coditios Documet referece: PPAS1(7) This is a importat documet. Please keep it i a safe place. Pesio Auity Policy Coditios Welcome to LV=, ad thak you for choosig our Pesio Auity.
More informationRepeating Decimals are decimal numbers that have number(s) after the decimal point that repeat in a pattern.
5.5 Fractios ad Decimals Steps for Chagig a Fractio to a Decimal. Simplify the fractio, if possible. 2. Divide the umerator by the deomiator. d d Repeatig Decimals Repeatig Decimals are decimal umbers
More informationUM USER SATISFACTION SURVEY 2011. Final Report. September 2, 2011. Prepared by. ers eresearch & Solutions (Macau)
UM USER SATISFACTION SURVEY 2011 Fial Report September 2, 2011 Prepared by ers eresearch & Solutios (Macau) 1 UM User Satisfactio Survey 2011 A Collaboratio Work by Project Cosultat Dr. Agus Cheog ers
More informationleasing Solutions We make your Business our Business
if you d like to discover how Bp paribas leasig Solutios Ca help you to achieve your goals please get i touch leasig Solutios We make your Busiess our Busiess We look forward to hearig from you you ca
More informationA powerful promise. The 21st Century Scholarship Covenant
A powerful promise The 21st Cetury Scholarship Coveat Keep your eye o the prize. We ll help you reach your goal. The greatest good you ca do for aother is ot just to share your riches, but to reveal to
More information1 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 informationCHAPTER 3 DIGITAL CODING OF SIGNALS
CHAPTER 3 DIGITAL CODING OF SIGNALS Computers are ofte used to automate the recordig of measuremets. The trasducers ad sigal coditioig circuits produce a voltage sigal that is proportioal to a quatity
More informationAGC 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 informationBasic Elements of Arithmetic Sequences and Series
MA40S PRECALCULUS UNIT G GEOMETRIC SEQUENCES CLASS NOTES (COMPLETED NO NEED TO COPY NOTES FROM OVERHEAD) Basic Elemets of Arithmetic Sequeces ad Series Objective: To establish basic elemets of arithmetic
More informationHow 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 information5: 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 informationAP Calculus AB 2006 Scoring Guidelines Form B
AP Calculus AB 6 Scorig Guidelies Form B The College Board: Coectig Studets to College Success The College Board is a otforprofit membership associatio whose missio is to coect studets to college success
More informationTaking DCOP to the Real World: Efficient Complete Solutions for Distributed MultiEvent Scheduling
Taig DCOP to the Real World: Efficiet Complete Solutios for Distributed MultiEvet Schedulig Rajiv T. Maheswara, Milid Tambe, Emma Bowrig, Joatha P. Pearce, ad Pradeep araatham Uiversity of Souther Califoria
More informationBaan Service Master Data Management
Baa Service Master Data Maagemet Module Procedure UP069A US Documetiformatio Documet Documet code : UP069A US Documet group : User Documetatio Documet title : Master Data Maagemet Applicatio/Package :
More informationGrow your business with savings and debt management solutions
Grow your busiess with savigs ad debt maagemet solutios A few great reasos to provide bak ad trust products to your cliets You have the expertise to help your cliets get the best rates ad most competitive
More informationChapter 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 information1 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 informationChapter 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 informationCollege of Nursing and Health care Professions
College of Nursig ad Health care Professios a history of excellece Grad Cayo Uiversity s College of Nursig ad Health Care Professios has bee providig a outstadig health care educatio for over 25 years.
More informationConservative treatment:
Coservative treatmet: Choosig ot to start dialysis T H E K I D N E Y F O U N D A T I O N O F C A N A D A 1 Coservative treatmet: Choosig ot to start dialysis You have the right to make your ow choices
More informationTrading the randomness  Designing an optimal trading strategy under a drifted random walk price model
Tradig the radomess  Desigig a optimal tradig strategy uder a drifted radom walk price model Yuao Wu Math 20 Project Paper Professor Zachary Hamaker Abstract: I this paper the author iteds to explore
More informationOnline Banking. Internet of Things
Olie Bakig & The Iteret of Thigs Our icreasigly iteretcoected future will mea better bakig ad added security resposibilities for all of us. FROM DESKTOPS TO SMARTWATCHS Just a few years ago, Americas coducted
More informationChapter 14 Nonparametric Statistics
Chapter 14 Noparametric Statistics A.K.A. distributiofree statistics! Does ot deped o the populatio fittig ay particular type of distributio (e.g, ormal). Sice these methods make fewer assumptios, they
More informationLesson 15 ANOVA (analysis of variance)
Outlie Variability betwee group variability withi group variability total variability Fratio Computatio sums of squares (betwee/withi/total degrees of freedom (betwee/withi/total mea square (betwee/withi
More informationA GUIDE TO BUILDING SMART BUSINESS CREDIT
A GUIDE TO BUILDING SMART BUSINESS CREDIT Establishig busiess credit ca be the key to growig your compay DID YOU KNOW? Busiess Credit ca help grow your busiess Soud paymet practices are key to a solid
More informationBuilding 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 informationBond Valuation I. What is a bond? Cash Flows of A Typical Bond. Bond Valuation. Coupon Rate and Current Yield. Cash Flows of A Typical Bond
What is a bod? Bod Valuatio I Bod is a I.O.U. Bod is a borrowig agreemet Bod issuers borrow moey from bod holders Bod is a fixedicome security that typically pays periodic coupo paymets, ad a pricipal
More informationA GUIDE TO LEVEL 3 VALUE ADDED IN 2013 SCHOOL AND COLLEGE PERFORMANCE TABLES
A GUIDE TO LEVEL 3 VALUE ADDED IN 2013 SCHOOL AND COLLEGE PERFORMANCE TABLES Cotets Page No. Summary Iterpretig School ad College Value Added Scores 2 What is Value Added? 3 The Learer Achievemet Tracker
More informationTopic 5: Confidence Intervals (Chapter 9)
Topic 5: Cofidece Iterval (Chapter 9) 1. Itroductio The two geeral area of tatitical iferece are: 1) etimatio of parameter(), ch. 9 ) hypothei tetig of parameter(), ch. 10 Let X be ome radom variable with
More informationCCH CRM Books Online Software Fee Protection Consultancy Advice Lines CPD Books Online Software Fee Protection Consultancy Advice Lines CPD
Books Olie Software Fee Fee Protectio Cosultacy Advice Advice Lies Lies CPD CPD facig today s challeges As a accoutacy practice, maagig relatioships with our cliets has to be at the heart of everythig
More informationExample 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 informationAllied Health Workforce Analysis Los Angeles Region
F u d e d b y & p r e p a r e d f o r : Allied Health Workforce Aalysis Los Ageles Regio Timothy Bates, M.P.P. Susa Chapma, Ph.D, R.N. M A Y 2 0 0 8 October 2007 A report from The Allied Health Care Workforce
More informationCHAPTER 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 informationTHE 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 informationCase 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 informationODBC. Getting Started With Sage Timberline Office ODBC
ODBC Gettig Started With Sage Timberlie Office ODBC NOTICE This documet ad the Sage Timberlie Office software may be used oly i accordace with the accompayig Sage Timberlie Office Ed User Licese Agreemet.
More informationSECTION 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 informationsummary of cover CONTRACT WORKS INSURANCE
1 SUMMARY OF COVER CONTRACT WORKS summary of cover CONTRACT WORKS INSURANCE This documet details the cover we ca provide for our commercial or church policyholders whe udertakig buildig or reovatio works.
More informationComparing Credit Card Finance Charges
Comparig Credit Card Fiace Charges Comparig Credit Card Fiace Charges Decidig if a particular credit card is right for you ivolves uderstadig what it costs ad what it offers you i retur. To determie how
More informationDepartment of Computer Science, University of Otago
Departmet of Computer Sciece, Uiversity of Otago Techical Report OUCS200609 Permutatios Cotaiig May Patters Authors: M.H. Albert Departmet of Computer Sciece, Uiversity of Otago Micah Colema, Rya Fly
More informationwhere: 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 informationOnesample test of proportions
Oesample 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 informationA Podiatrists Guide to Earning More, Working Less and Enjoying What You Do Each Day. Tyson E. Franklin
A Podiatrists Guide to Earig More, Workig Less ad Ejoyig What You Do Each Day Tyso E. Frakli MOVING OUT OF YOUR COMFORT ZONE 1Years ago, if someoe had told me I d be livig i Cairs oe day I would have laughed.
More informationBasic Current Account
Curret Accouts Basic Curret Accout Applicatio form Please fill i the form usig BLOCK CAPITALS ad black ik. Tick ay boxes which apply. Satader is able to provide literature i alterative formats. The formats
More informationCHAPTER 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 informationIntroducing 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 easytoread statemet. It provides a overview of your accout ad a complete
More informationThe 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 informationA Parent s Guide to College and Career Readiness:
A Paret s Guide to College ad Career Readiess: A guide to help you support your child s dreams Produced by the Uiversity of Miesota College Readiess Cosortium i partership with the Uiversity YMCA 1 INTRODUCTION
More informationFactoring x n 1: cyclotomic and Aurifeuillian polynomials Paul Garrett <garrett@math.umn.edu>
(March 16, 004) Factorig x 1: cyclotomic ad Aurifeuillia polyomials Paul Garrett Polyomials of the form x 1, x 3 1, x 4 1 have at least oe systematic factorizatio x 1 = (x 1)(x 1
More informationResearch 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 informationADMISSION AND REGISTRATION
14 Admissio ad Registratio 2 C H A P T E R ADMISSION AND REGISTRATION ADMISSION INFORMATION Admissio to Ohloe College is ope to ayoe who is a high school graduate, has a high school equivalecy certificate
More informationHandling. 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 informationwww.nacacnet.org Guiding the Way to Higher Education: StepbyStep To College Workshops for Students Late High School Curriculum Grades 11 and 12
www.acacet.org Guidig the Way to Higher Educatio: StepbyStep To College Workshops for Studets Late High School Curriculum Grades 11 ad 12 www.acacet.org Copyright 2008 These materials are copyrighted
More informationHOSPITAL NURSE STAFFING SURVEY
2012 Ceter for Nursig Workforce St udies HOSPITAL NURSE STAFFING SURVEY Vacacy ad Turover Itroductio The Hospital Nurse Staffig Survey (HNSS) assesses the size ad effects of the ursig shortage i hospitals,
More informationSavings and Retirement Benefits
60 Baltimore Couty Public Schools offers you several ways to begi savig moey through payroll deductios. Defied Beefit Pesio Pla Tax Sheltered Auities ad Custodial Accouts Defied Beefit Pesio Pla Did you
More informationSetting 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 informationMannWhitney U 2 Sample Test (a.k.a. Wilcoxon Rank Sum Test)
NoParametric ivariate Statistics: WilcoxoMaWhitey 2 Sample Test 1 MaWhitey 2 Sample Test (a.k.a. Wilcoxo Rak Sum Test) The (Wilcoxo) MaWhitey (WMW) test is the oparametric equivalet of a pooled
More informationPractice Problems for Test 3
Practice Problems for Test 3 Note: these problems oly cover CIs ad hypothesis testig You are also resposible for kowig the samplig distributio of the sample meas, ad the Cetral Limit Theorem Review all
More informationWE KEEP GOOD COMPANY REVITALIZING STORES FOR BIG BRAND FRANCHISES CFG. Recapitalization. Growth Capital CAROLINA FINANCIAL GROUP
REVITALIZING STORES FOR BIG BRAND FRANCHISES Recapitalizatio Frachise ad Restaurats Growth Capital I ve spet my career buildig valuable restaurat frachises for some of the top QSR brads i America. We eeded
More informationPSYCHOLOGICAL STATISTICS
UNIVERSITY OF CALICUT SCHOOL OF DISTANCE EDUCATION B Sc. Cousellig Psychology (0 Adm.) IV SEMESTER COMPLEMENTARY COURSE PSYCHOLOGICAL STATISTICS QUESTION BANK. Iferetial statistics is the brach of statistics
More information