Effects of Math Tutoring


 Ethelbert Martin
 1 years ago
 Views:
Transcription
1 Requestor: Math Deartment Researcher(s): Steve Blohm Date: 6/30/15 Title: Effects of Math Tutoring Effects of Math Tutoring The urose of this study is to measure the effects of math tutoring at Cabrillo College. This is an observational study in which we looked at student grades, success rates, and comletion rates for students who received tutoring and those who did not in all math classes in which at least one student was tutored. From a research oint of view, it would have been better to use an exeriment where we could examine the effects of tutoring while holding other variables constant. In order to erform an exeriment to measure the effect of math tutoring, we would need to randomly select students to be tutored from a ool of students who seek tutoring, while denying other students tutoring. However, due to ractical, ethical, and regulatory concerns about denying tutoring students to students, an exerimental aroach was not ossible. The direct comarisons between those receiving tutoring and those not receiving tutoring while useful are subject to selfselection bias. To hel control for bias from students selfselecting to articiate in tutoring, regression and roensity score matching (PSM) techniques were emloyed to equate articiants and nonarticiants on a variety of background variables. The results are of this study are consistent with the belief that tutoring is helful for students Samle: The samle analyzed consists of students who used tutoring at the MLC from summer 2009 through summer 2013 along with those who were enrolled in a course where at least one of the students used tutoring. We looked at six courses at Cabrillo where enrollment and tutoring use were both relatively high comared to other courses. The courses we looked at were Statistics (Math 12), Intermediate Algebra (Math 152), Basic Algebra (Math 154), Essential Mathematics (Math 254A), Precalculus (Math 4), and Calculus (Math 5A). Many students at Cabrillo have had difficulty succeeding in Intermediate Algebra. We look in detail at the relationshi between hours tutored and success in this course while controlling for other factors that are redictive of success. The following figure looks at the success rates in each class for these three grous 1. Students who did not log tutoring time or log time at the Math Learning Center (MLC)  Nothing 2. Students who used the MLC, but did not use tutoring MLC Only 3. Student who used some tutoring  Tutoring 1
2 Figure 1: Percentage of Math Students Earning a C or Better In every case, those who were tutored outerform those who were not. The samle sizes were large so even small difference would show u as significant. However, in many cases the differences are substantial. For examle, recalculus students who were tutored succeed 58% of the time and those who were not succeeded 48% of the time. Below we will investigate the effect of tutoring among secific among different gender, ethnic grous, and other secial oulations. 2
3 Figure 2: Percentage of Math Students Earning a C or Better by Gender Both female and male students erform better with tutoring than without. Female students tend to outerform male students in all cases excet for those not tutored in Calculus I. In every case other than Math 154, Elementary Algebra, the higher success rate for female tutored students was statistically significant at the 0.05 level of significance. For male students the higher success rate for tutored students was statistically significant in all cases other than Math 12 (Statistics) and Math 5A (Calculus). ChiSquare details are in Aendix A. 3
4 Figure 3: Percentage of Math Students Earning a C or Better by URM Status While underreresented minority students (URM) do tend to have lower success rates comared to other students, those URM students who receive tutoring erform better than URM students who do not receive tutoring. For Math 152, Math 254A and Math 5A the higher success rates are statistically significant at the 0.05 level significance. This information is based on a twotailed significance level where the hyothesis is whether or not success rates are different for tutored students. If we were to instead consider these onetailed tests where our hyothesis is whether or not tutored students have a higher success rate then the results would be significant for all six courses 1. ChiSquare details are in Aendix A. 1 While a chisquare test is generally only a twotailed test because the chisquare statistic is not symmetrical, in this case our chisquare is mathematically equivalent to a Zstatistic. We can therefor take half the value of the two tailed test and comare that to the 0.05 level of significance for a onetailed test. 4
5 Percent of Students Enrolled in Intermediate Algebra that Used Tutoring by Ethnicity 29.82% 23.68% 23.34% 25.63% 24.14% 18.68% 18.52% 13.45% 8.11% American Indian, Alaskan Nativ Asian Black Non Hisanic Filiino Latino Multile Ethnicities Pacific Islander Unknown White Non Hisanic Figure 4: Tutoring Use By Ethnicity Of the 8,082 Students enrolled in math 152 over the years studied, 1,757 (21.74%) used tutoring % of underreresented minority (URM) students used tutoring for intermediate algebra while 23.95% of nonurm students used tutoring. The table below shows tutoring use by ethnicity for Math 152, Intermediate Algebra. While URM students in general used less tutoring than other students, black students used tutoring at the highest rate. 5
6 Figure 5: roensity score matching analysis of tutoring use The above chart shows erformance differences between students who were tutored and a comarison grou of students who were not tutored but have similar attributes based on one to many roensity score matching (PSM). The criteria used for matching were gender, ethnicity (as a binary variable indicating URM or not), age, and rior overall grade oint average (GPA). The chart shows that on average those who were tutored had better success rates than those who were not. The chart also shows a 95% confidence interval for the difference. If the confidence interval does not include zero then we can be at least 95% confident that the difference did not occur by random chance. In every case excet for Math 154, Elementary Algebra the results were statistically significant. The largest difference in success rates between the two grous ids for Math 254, Prealgebra. 6
7 Figure 6: Distribution of Hours Tutored for Math 152 Intermediate Algebra For Math 152, Intermediate Algebra, the maximum number of hours someone was tutored for a semester was 72 hours and the minimum was less than an hour. The mean time for tutoring was about 3 hours er semester with a standard deviation of 5.5 hours. But as can be seen in the histogram above, the data are not normally distributed. The median for hours tutored was 1.15 hours; 50% of students tutored received 1.15 or less hours of tutoring in the semester. Seventy five ercent of students tutored received 3.16 or less hours of tutoring during the semester. Looking at these numbers searately for those who succeeded vs those who did not succeed we see the average number of tutoring hours for those who succeed was 3.58 and the average number of tutoring hours for those who do not succeed was This difference is statistically significant at <
8 Logistic Regression We used a Logistic Regression to estimate the number of tutoring hours required to succeed in Intermediate algebra and to attemt to at least artly control for selfselection bias. Below we estimate the ln(odds of succeeding) using hours tutored, gender, URM status, tutoring use and overall GPA. = robability of succeeding in Math ) = α + β 1x 1 + β 2 x 2 + β 3 x 3 + β 4 x 4 + β 5 x 5 + ε 1 ) = α + TutoringHoursx 1 + URMx 2 + Genderx 3 + GPAx 4 + UseTutorx 5 + ε Variables in the Equation Beta Std. Error Wald df PValue Constant P < Tutoring Hours P < URM (Y = 1) P < Gender (F = 1) P < Overall GPA P < Use Tutoring (Y = 1) P = Because we are looking at the log odds of succeeding, it is difficult to interret the Betas. However, ositive values mean that the variable has a ositive influence on succeeding and negative values mean the variable has a negative imact on succeeding. Of the variables coded dichotomously, URM status has the largest influence. We could use the equation below to redict the robability of success for a articular student that student s information for each of the variable in the equation. 1 ) = x x x x x 5 + ε 8
9 Alternatively we could use the equation to estimate the Number of Tutoring Hours Required to Succeed in Intermediate Algebra The equation below can be used to redict the ln(odds of succeeding) using hours tutored, gender, URM status and overall GPA as a covariate. = robability of succeeding in Math ) = α + β 1x 1 + β 2 x 2 + β 3 x 3 + β 4 x 4 + ε 1 ) = α + TutoringHoursx 1 + URMx 2 + Genderx 3 + GPAx 4 + ε Variables in the Equation Std. Error Wald df PValue Beta Constant P = Tutoring Hours P < URM (Y = 1) P < Gender (F = 1) P = Overall GPA P < ) = x x x x 4 + ε Note: Samle only includes those who used tutoring. 9
10 If we want to estimate the number of tutoring hours required for success for a grou, it is simler to exclude GPA (a continuous variable) as a covariate. Here we estimate the ln(odds of succeeding) using hours tutored, gender, and URM status. = robability of succeeding in Math ) = α + β 1x 1 + β 2 x 2 + β 3 x 3 + ε 1 ) = α + TutoringHoursx 1 + URMx 2 + Genderx 3 + ε Variables in the Equation Beta Std. Error Wald df PValue Constant = Tutoring Hours < URM (Y = 1) < Gender (F = 1) = ) = x x x 3 + ε Note: Samle only includes those who used tutoring. 10
11 Now we can estimate the number of tutoring hours required for success in Math 152 for a articular oulation of interest. For examle, if we want to know the number of hours of tutoring estimated for underreresented minority male success in Intermediate Algebra we could use the equation below. The equation for an underreresented minority male simlifies to: 1 ) = x 1 + ε Among the 1,467 Male URM students who were not tutored, 560 succeeded (38%) Among the 316 Male URM students who were tutored at all, 129 succeeded (41%) For = 0.5 we estimate that a URM male needs 8.3 hours of tutoring (about 0.5 hours er week) 29 Male URM students were tutored for at least 8.3 hours and 17 succeeded (59%) For = 0.75 we estimate that a URM male needs 29.5 hours of tutoring (about 2 hours er week) The two URM male students who had at least 29.5 hours of tutoring both succeeded. 11
12 English Placement Scores Other covariates such as English Placement scores could be used. However, comaring English Placement scores for those who used tutoring and those who do not use tutoring we see no significant difference. Used Tutoring N Mean English Placement Score Std. Deviation Std. Error Mean No 5, Yes 1, The difference of 0.37 is not significant ( = 0.149). Given that the samle size is over 6,000, this is evidence that English lacement scores are not related to the decision to use tutoring for Math 152, Intermediate Algebra. There is a significant difference between English lacement scores for those who succeed in Math 152, Intermediate Algebra vs those who do not succeed in Math 152; English lacement is a significant redictor of success in Math 152. Success in Math152 N Mean English Placement Score Std. Deviation Std. Error Mean No 3, Yes 3, The difference of 2.5 is statistically significant ( < 0.001) When we add English lacement scores to the equation it is a significant redictor of success. However, the fit of the equation does not change in any ractical way and the samle size is reduced by almost 1,700 students when we use this information. The rimary imlication is that more tutoring did aear to lead to greater success in math classes in general. Even those students who use tutoring tend to use very little. Encouraging students to use tutoring and to use it often would likely lead to greater success in math courses. Some of the underreresented minority grous used tutoring at a disroortionately lower rate than their white counterarts. In articular, Latino, Filiino, and Pacific Islander students used tutoring at less than 80% of the rate of use by White students. Other nonurm Asian students tended to use less tutoring as well. No more than 30% of any ethnic grou used tutoring and this seaks to the fact that all students, regardless of their background, could robably benefit by using more tutoring. 12
13 Aendix A Female Students Course Name ChiSquare N df Sig. (2sided) MATH , MATH , MATH , MATH254A , MATH MATH5A Table 1: ChiSquare analysis tutored vs nontutored female students. Male Students Course Name ChiSquare N df Sig. (2sided) MATH , MATH , MATH , MATH254A MATH , MATH5A , Table 2: ChiSquare analysis tutored vs nontutored male students. URM Students Course Name ChiSquare N df Sig. (2sided) MATH , MATH , MATH , MATH254A , MATH MATH5A Table 3: ChiSquare analysis tutored vs nontutored URM students. 13
Office of Institutional Research & Planning
NECC Northern Essex Community College NECC College Math Tutoring Center Results Spring 2011 The College Math Tutoring Center at Northern Essex Community College opened its doors to students in the Spring
More informationChapter 9, Part B Hypothesis Tests. Learning objectives
Chater 9, Part B Hyothesis Tests Slide 1 Learning objectives 1. Able to do hyothesis test about Poulation Proortion 2. Calculatethe Probability of Tye II Errors 3. Understand ower of the test 4. Determinethe
More informationHOMEWORK (due Fri, Nov 19): Chapter 12: #62, 83, 101
Today: Section 2.2, Lesson 3: What can go wrong with hyothesis testing Section 2.4: Hyothesis tests for difference in two roortions ANNOUNCEMENTS: No discussion today. Check your grades on eee and notify
More informationTheoretical comparisons of average normalized gain calculations
PHYSICS EDUCATIO RESEARCH All submissions to PERS should be sent referably electronically to the Editorial Office of AJP, and then they will be forwarded to the PERS editor for consideration. Theoretical
More informationThe impact of metadata implementation on webpage visibility in search engine results (Part II) q
Information Processing and Management 41 (2005) 691 715 www.elsevier.com/locate/inforoman The imact of metadata imlementation on webage visibility in search engine results (Part II) q Jin Zhang *, Alexandra
More informationNormally Distributed Data. A mean with a normal value Test of Hypothesis Sign Test Paired observations within a single patient group
ANALYSIS OF CONTINUOUS VARIABLES / 31 CHAPTER SIX ANALYSIS OF CONTINUOUS VARIABLES: COMPARING MEANS In the last chater, we addressed the analysis of discrete variables. Much of the statistical analysis
More informationIt is important to be very clear about our definitions of probabilities.
Use Bookmarks for electronic content links 7.6 Bayesian odds 7.6.1 Introduction The basic ideas of robability have been introduced in Unit 7.3 in the book, leading to the concet of conditional robability.
More informationSTANDARD REPORTS FOR THE ABBREVIATED FORM OF THE ETS PROFICIENCY PROFILE TEST
STANDARD REPORTS FOR THE ABBREVIATED FORM OF THE ETS PROFICIENCY PROFILE TEST P&S DMS 71 Individual Student Score Report Scores for: Student Test Date: 9/6/26 I.D. Number: 123456789 Form Code: 4BMA1A1P
More informationTesting Hypotheses using SPSS
Is the mean hourly rate of male workers $2.00? TTest OneSample Statistics Std. Error N Mean Std. Deviation Mean 2997 2.0522 6.6282.2 OneSample Test Test Value = 2 95% Confidence Interval Mean of the
More informationConfidence Intervals for CaptureRecapture Data With Matching
Confidence Intervals for CatureRecature Data With Matching Executive summary Caturerecature data is often used to estimate oulations The classical alication for animal oulations is to take two samles
More informationEffect Sizes Based on Means
CHAPTER 4 Effect Sizes Based on Means Introduction Raw (unstardized) mean difference D Stardized mean difference, d g Resonse ratios INTRODUCTION When the studies reort means stard deviations, the referred
More informationPOL 345: Quantitative Analysis and Politics
POL 345: Quantitative Analysis and Politics Precet Handout 8 Week 10 (Verzani Chaters 7 and 8: 7.5, 8.6.18.6.2) Remember to comlete the entire handout and submit the recet questions to the Blackboard
More informationDream Recall and Political Ideology: Results of a Demographic Survey
Dream Recall and Political Ideology: Results of a Demograhic Survey Kelly Bulkeley The Graduate Theological Union This reort resents findings from a survey of 2992 demograhically diverse American adults
More informationCoCurricular Activities and Academic Performance A Study of the Student Leadership Initiative Programs. Office of Institutional Research
CoCurricular Activities and Academic Performance A Study of the Student Leadership Initiative Programs Office of Institutional Research July 2014 Introduction The Leadership Initiative (LI) is a certificate
More informationHigh School Science and Mathematics Course Enrollment and Performance of Students who Attended the International Baccalaureate Middle Years Programme
High School Science and Mathematics Course Enrollment and Performance of Students who Attended the International Baccalaureate Middle Years Programme February 2013 Julie H. Wade and Natalie L. Wolanin
More informationA Multivariate Statistical Analysis of Stock Trends. Abstract
A Multivariate Statistical Analysis of Stock Trends Aril Kerby Alma College Alma, MI James Lawrence Miami University Oxford, OH Abstract Is there a method to redict the stock market? What factors determine
More informationMaximizing the Area under the ROC Curve using Incremental Reduced Error Pruning
Maximizing the Area under the ROC Curve using Incremental Reduced Error Pruning Henrik Boström Det. of Comuter and Systems Sciences Stockholm University and Royal Institute of Technology Forum 100, 164
More informationBenefits of a High School Core Curriculum
COLLEGE READINESS Benefits of a High School Curriculum Since the publication of A Nation at Risk, ACT has recommended that students take a core curriculum in high school in order to be prepared for collegelevel
More informationStatistics for Management IISTAT 362Final Review
Statistics for Management IISTAT 362Final Review Multiple Choice Identify the letter of the choice that best completes the statement or answers the question. 1. The ability of an interval estimate to
More informationSegmentation Modeling or Classification and Regression Trees (CART)
Segmentation Modeling or Classification and Regression Trees (CART) Presented by Keith Wurtz Senior Research Analyst Chaffey Community College Keith.wurtz@chaffey.edu Examples of Segmentation Modeling
More informationCRJ Doctoral Comprehensive Exam Statistics Friday August 23, :00pm 5:30pm
CRJ Doctoral Comprehensive Exam Statistics Friday August 23, 23 2:pm 5:3pm Instructions: (Answer all questions below) Question I: Data Collection and Bivariate Hypothesis Testing. Answer the following
More informationFrequentist vs. Bayesian Statistics
Bayes Theorem Frequentist vs. Bayesian Statistics Common situation in science: We have some data and we want to know the true hysical law describing it. We want to come u with a model that fits the data.
More informationEvaluating a WebBased Information System for Managing Master of Science Summer Projects
Evaluating a WebBased Information System for Managing Master of Science Summer Projects Till Rebenich University of Southamton tr08r@ecs.soton.ac.uk Andrew M. Gravell University of Southamton amg@ecs.soton.ac.uk
More informationBeyond the F Test: Effect Size Confidence Intervals and Tests of Close Fit in the Analysis of Variance and Contrast Analysis
Psychological Methods 004, Vol. 9, No., 164 18 Coyright 004 by the American Psychological Association 108989X/04/$1.00 DOI: 10.1037/108989X.9..164 Beyond the F Test: Effect Size Confidence Intervals
More informationCBus Voltage Calculation
D E S I G N E R N O T E S CBus Voltage Calculation Designer note number: 3121256 Designer: Darren Snodgrass Contact Person: Darren Snodgrass Aroved: Date: Synosis: The guidelines used by installers
More informationThe risk of using the Q heterogeneity estimator for software engineering experiments
Dieste, O., Fernández, E., GarcíaMartínez, R., Juristo, N. 11. The risk of using the Q heterogeneity estimator for software engineering exeriments. The risk of using the Q heterogeneity estimator for
More information2005 Public High School Graduates Report Massachusetts College of Art, Fall 2005
2005 Public High School Graduates Report Massachusetts College of Art, Fall 2005 About this report This report contains student characteristics and initial outcomes for 2005 MA public high school graduates
More informationAn important observation in supply chain management, known as the bullwhip effect,
Quantifying the Bullwhi Effect in a Simle Suly Chain: The Imact of Forecasting, Lead Times, and Information Frank Chen Zvi Drezner Jennifer K. Ryan David SimchiLevi Decision Sciences Deartment, National
More informationMonitoring Frequency of Change By Li Qin
Monitoring Frequency of Change By Li Qin Abstract Control charts are widely used in rocess monitoring roblems. This aer gives a brief review of control charts for monitoring a roortion and some initial
More informationUsing SPSS for Multiple Regression. UDP 520 Lab 7 Lin Lin December 4 th, 2007
Using SPSS for Multiple Regression UDP 520 Lab 7 Lin Lin December 4 th, 2007 Step 1 Define Research Question What factors are associated with BMI? Predict BMI. Step 2 Conceptualizing Problem (Theory) Individual
More informationData Mining Techniques Chapter 5: The Lure of Statistics: Data Mining Using Familiar Tools
Data Mining Techniques Chapter 5: The Lure of Statistics: Data Mining Using Familiar Tools Occam s razor.......................................................... 2 A look at data I.........................................................
More informationPhysical Chemistry. Probability of event. Probability of a sequence. Macroscopic versus microscopic
Macroscoic versus microscoic Physical Chemistry Lecture Random walks; microscoic theory of diffusion Fick s laws describe time evolution macroscoically o not secifically consider a single molecule (although
More informationThe Math TLC Master s in Mathematics for Secondary Teachers Program
Math TLC Master s Application Form Page 1 The Math TLC Master s in Mathematics for Secondary Teachers Program Electronic Application Form Instructions Save your completed application a filename of the
More informationLoglikelihood and Confidence Intervals
Stat 504, Lecture 3 Stat 504, Lecture 3 2 Review (contd.): Loglikelihood and Confidence Intervals The likelihood of the samle is the joint PDF (or PMF) L(θ) = f(x,.., x n; θ) = ny f(x i; θ) i= Review:
More informationMeasures of Clinic Systems in Clinic Surveys
use is not associated with better diabetes care Patrick J. O Connor, MD, MPH, A. Lauren Crain, PhD, Leif I. Solberg, MD, Stehen E. Asche, MA, William A. Rush, PhD, Robin R. Whitebird, PhD, MSW Electronic
More informationRisk and Return. Sample chapter. e r t u i o p a s d f CHAPTER CONTENTS LEARNING OBJECTIVES. Chapter 7
Chater 7 Risk and Return LEARNING OBJECTIVES After studying this chater you should be able to: e r t u i o a s d f understand how return and risk are defined and measured understand the concet of risk
More informationD.Sailaja, K.Nasaramma, M.Sumender Roy, Venkateswarlu Bondu
Predictive Modeling of Customers in Personalization Alications with Context D.Sailaja, K.Nasaramma, M.Sumender Roy, Venkateswarlu Bondu Nasaramma.K is currently ursuing her M.Tech in Godavari Institute
More informationMultivariate Models of Student Success
Multivariate Models of Student Success RP Group/CISOA Conference April 28, 2009 Granlibakken, Lake Tahoe Dr. Matt Wetstein Dean of Planning, Research and Institutional Effectiveness San Joaquin Delta College
More informationBinary Logistic Regression
Binary Logistic Regression Main Effects Model Logistic regression will accept quantitative, binary or categorical predictors and will code the latter two in various ways. Here s a simple model including
More informationStudents' Opinion about Universities: The Faculty of Economics and Political Science (Case Study)
Cairo University Faculty of Economics and Political Science Statistics Department English Section Students' Opinion about Universities: The Faculty of Economics and Political Science (Case Study) Prepared
More informationRunning head: MATH LEVELS OF ECONOMICS STUDENTS. Math Levels of Economics Students
Running head: MATH LEVELS OF ECONOMICS STUDENTS Math Levels of Economics Students Rick Fillman Institutional Research Analyst Planning and Research Office December 2010 Page 2 of 6 Introduction Many fouryear
More informationSouth Dakota DOE 20142015 Report Card
Performance Indicators School Performance Index District Classification:  Exemplary Schools 1 / 24 Schools 4.17% Status Schools 1 / 24 Schools 4.17% Progressing Schools 19 / 24 Schools * No bar will display
More informationFactors Influencing the Impact of ICTuse on Students Learning
Factors Influencing the Imact of ICTuse on Students Learning The Proceedings of IRC 2008 Factors Influencing the Imact of ICTuse on Students Learning Nancy Law, The University of Hong Kong, nlaw@hku.hk
More informationIndependent t Test (Comparing Two Means)
Independent t Test (Comparing Two Means) The objectives of this lesson are to learn: the definition/purpose of independent ttest when to use the independent ttest the use of SPSS to complete an independent
More informationA Brief Introduction to Design of Experiments
J. K. TELFORD D A Brief Introduction to Design of Exeriments Jacqueline K. Telford esign of exeriments is a series of tests in which uroseful changes are made to the inut variables of a system or rocess
More informationHYPOTHESIS TESTING FOR THE PROCESS CAPABILITY RATIO. A thesis presented to. the faculty of
HYPOTHESIS TESTING FOR THE PROESS APABILITY RATIO A thesis resented to the faculty of the Russ ollege of Engineering and Technology of Ohio University In artial fulfillment of the requirement for the degree
More informationSouth Dakota DOE 20132014 Report Card
School Classification: Focus Title I Designation: Schoolwide Performance Indicators * No bar will display at the school or district level if the subgroup does not meet minimum size for reporting purposes.
More informationCHAPTER 11: FACTS ABOUT THE CHI SQUARE DISTRIBUTION
CHAPTER 11: FACTS ABOUT THE CHI SQUARE DISTRIBUTION Exercise 1. If the number of degrees of freedom for a chisquare distribution is 25, what is the population mean and standard deviation? mean = 25 and
More informationContextBased Network Estimation for EnergyEfficient Ubiquitous Wireless Connectivity
IEEE TRANSACTIONS ON MOBILE COMPUTING, MANUSCRIPT ID ContextBased Network Estimation for EnergyEfficient Ubiquitous Wireless Connectivity Ahmad Rahmati, Student Member, and Lin Zhong, Member, IEEE Abstract
More informationThe Portfolio Characteristics and Investment Performance of Bank Loan Mutual Funds
The Portfolio Characteristics and Investment Performance of Bank Loan Mutual Funds Zakri Bello, Ph.. Central Connecticut State University 1615 Stanley St. New Britain, CT 06050 belloz@ccsu.edu 8608323262
More informationEpidemiologyBiostatistics Exam Exam 2, 2001 PRINT YOUR LEGAL NAME:
EpidemiologyBiostatistics Exam Exam 2, 2001 PRINT YOUR LEGAL NAME: Instructions: This exam is 30% of your course grade. The maximum number of points for the course is 1,000; hence this exam is worth 300
More informationUsing Predictive Analytics to Understand Student Loan Defaults
Using Predictive Analytics to Understand Student Loan Defaults RP Group Conference April 8, 2016 Tina Merlino, Research Analyst Lindsay Brown, Research Analyst Tina Lent, Director of Financial Aid, Scholarships
More informationSimple Linear Regression One Binary Categorical Independent Variable
Simple Linear Regression Does sex influence mean GCSE score? In order to answer the question posed above, we want to run a linear regression of sgcseptsnew against sgender, which is a binary categorical
More informationOn the predictive content of the PPI on CPI inflation: the case of Mexico
On the redictive content of the PPI on inflation: the case of Mexico José Sidaoui, Carlos Caistrán, Daniel Chiquiar and Manuel RamosFrancia 1 1. Introduction It would be natural to exect that shocks to
More informationManaging specific risk in property portfolios
Managing secific risk in roerty ortfolios Andrew Baum, PhD University of Reading, UK Peter Struemell OPC, London, UK Contact author: Andrew Baum Deartment of Real Estate and Planning University of Reading
More informationUnobservable Selection and Coefficient Stability: Theory and Evidence
Unobservable Selection and Coefficient Stability: Theory and Evidence Emily Oster Brown University and NBER November 24, 2014 Abstract A common heuristic for evaluating robustness of results to omitted
More informationA MOST PROBABLE POINTBASED METHOD FOR RELIABILITY ANALYSIS, SENSITIVITY ANALYSIS AND DESIGN OPTIMIZATION
9 th ASCE Secialty Conference on Probabilistic Mechanics and Structural Reliability PMC2004 Abstract A MOST PROBABLE POINTBASED METHOD FOR RELIABILITY ANALYSIS, SENSITIVITY ANALYSIS AND DESIGN OPTIMIZATION
More informationWednesday PM. Multiple regression. Multiple regression in SPSS. Presentation of AM results Multiple linear regression. Logistic regression
Wednesday PM Presentation of AM results Multiple linear regression Simultaneous Stepwise Hierarchical Logistic regression Multiple regression Multiple regression extends simple linear regression to consider
More informationThe Advantage of Timely Intervention
Journal of Exerimental Psychology: Learning, Memory, and Cognition 2004, Vol. 30, No. 4, 856 876 Coyright 2004 by the American Psychological Association 02787393/04/$12.00 DOI: 10.1037/02787393.30.4.856
More informationSTUDIES ON DYNAMIC VISCOSITY CHANGES OF THE ENGINE S LUBRICATION OIL DEPENDING ON THE PRESSURE
Journal of KONES Powertrain and Transort, Vol. 20, No. 4 2013 STUDIES ON DYNAMIC VISCOSITY CHANGES OF THE ENGINE S LUBRICATION OIL DEPENDING ON THE PRESSURE Grzegorz Sikora Gdynia Maritime University Deartment
More informationAlabama A&M University Student Academic Program Assessment Mechanical Engineering
I. Alabama A&M University Degree program type: 1 Undergraduate 14 100% 2 Graduate 0 0% Mean 1.00 Gender: 1 Male 9 64% 2 Female 5 36% Mean 1.36 Age group: 1 1820 0 0% 2 2123 10 71% 3 2425 3 21% 4 2630
More informationSimple Linear Regression Chapter 11
Simple Linear Regression Chapter 11 Rationale Frequently decisionmaking situations require modeling of relationships among business variables. For instance, the amount of sale of a product may be related
More informationACES. Report Requested: Study ID: R090xxx. Placement Validity Report for CLEP Sample ADMITTED CLASS EVALUATION SERVICE TM
ACES Report Requested: 08012009 Study ID: R090xxx Placement Validity Report for CLEP Sample Your College Board Validity Report is designed to assist your institution in validating your placement decisions.
More informationStudent s Name: SF ID#:
SF2UF Bridge Program at Santa Fe College Application for Admission Completed applications (pages 13) may be mailed or delivered to: Dr. Beatriz Gonzalez, Director, SF2UF Bridge Program at Santa Fe College,
More informationSTAT 5817: Logistic Regression and Odds Ratio
A cohort of people is a group of people whose membership is clearly defined. A prospective study is one in which a cohort of people is followed for the occurrence or nonoccurrence of specified endpoints
More informationThe University of Memphis Department of Social Work Social Work Information Form
The University of Memphis Department of Social Work Social Work Information Form The Department of Social Work wants to know something about each of the students who is selecting Social Work to be her/his
More informationTo open the survey, please enter your School's ID number in the box below and click on the "Login" button.
To open the survey, please enter your School's ID number in the box below and click on the "Login" button. If you do not know your school's ID, click here. Note: Do not enter your personal student ID below,
More informationGlendale College Conrad Amba: Research and Planning Unit Edward Karpp: Research and Planning Unit Jonn Reyes Aque: Counseling Faculty
Assessing TwoYear College Student Athlete Retention and Persistence Rates Glendale College Conrad Amba: Research and Planning Unit Edward Karpp: Research and Planning Unit Jonn Reyes Aque: Counseling
More informationAnalysis of Effectiveness of Web based E Learning Through Information Technology
International Journal of Soft Comuting and Engineering (IJSCE) Analysis of Effectiveness of Web based E Learning Through Information Technology Anand Tamrakar, Kamal K. Mehta AbstractAdvancements of
More informationACES. Report Requested: Study ID: R08xxxx. Placement Validity Report for ACCUPLACER Sample ADMITTED CLASS EVALUATION SERVICE TM
ACES Report Requested: 02012008 Study ID: R08xxxx Placement Validity Report for ACCUPLACER Sample Your College Board Validity Report is designed to assist your institution in validating your placement
More informationPartialOrder Planning Algorithms todomainfeatures. Information Sciences Institute University ofwaterloo
Relating the Performance of PartialOrder Planning Algorithms todomainfeatures Craig A. Knoblock Qiang Yang Information Sciences Institute University ofwaterloo University of Southern California Comuter
More informationStatic and Dynamic Properties of Smallworld Connection Topologies Based on Transitstub Networks
Static and Dynamic Proerties of Smallworld Connection Toologies Based on Transitstub Networks Carlos Aguirre Fernando Corbacho Ramón Huerta Comuter Engineering Deartment, Universidad Autónoma de Madrid,
More informationMore on the correct use of omnibus tests for normality
Economics Letters 90 (2006) 304 309 www.elsevier.com/locate/econbase More on the correct use of omnibus tests for normality Geoffrey Poitras Simon Fraser University, Burnaby, B.C., Canada VSA ls6 Received
More informationHypothesis Testing Level I Quantitative Methods. IFT Notes for the CFA exam
Hypothesis Testing 2014 Level I Quantitative Methods IFT Notes for the CFA exam Contents 1. Introduction... 3 2. Hypothesis Testing... 3 3. Hypothesis Tests Concerning the Mean... 10 4. Hypothesis Tests
More informationFREQUENCIES OF SUCCESSIVE PAIRS OF PRIME RESIDUES
FREQUENCIES OF SUCCESSIVE PAIRS OF PRIME RESIDUES AVNER ASH, LAURA BELTIS, ROBERT GROSS, AND WARREN SINNOTT Abstract. We consider statistical roerties of the sequence of ordered airs obtained by taking
More informationDuring this course we use two tests: a ttest and a chisquare test.
Statistical tests in Mlwin During this course we use two tests: a ttest and a chisquare test. Ttest A ttest is based on a tdistribution with a certain number of degrees of freedom (notation: df).
More informationProgram Review Data Packet
QUEENSBOROUGH COMMUNITY COLLEGE Program Review Data Packet 11/7/2014 Table of Contents A. Enrollment and Student Profile Student Profile & Headcount 1 Headcount by Part time/full time Status and FTE 2
More informationApplications of Regret Theory to Asset Pricing
Alications of Regret Theory to Asset Pricing Anna Dodonova * Henry B. Tiie College of Business, University of Iowa Iowa City, Iowa 522421000 Tel.: +13193379958 Email address: annadodonova@uiowa.edu
More informationThe Influence of a Summer Bridge Program on College Adjustment and Success: The Importance of Early Intervention and Creating a Sense of Community
The Influence of a Summer Bridge Program on College Adjustment and Success: The Importance of Early Intervention and Creating a Sense of Community Michele J. Hansen, Ph.D., Director of Assessment, University
More informationSchool of Nursing Faculty Salary Equity Report and Action Plan
July 1, 2015 School of Nursing Faculty Salary Equity Report and Action Plan Shari L. Dworkin, Ph.D., M.S. Associate Dean for Academic Affairs Overview: In 2012, then UC President Mark Yudof charged each
More informationAdministrative Council July 28, 2010 Presented by Nancy McNerney Institutional Effectiveness Planning and Research
Administrative Council July 28, 2010 Presented by Nancy McNerney Institutional Effectiveness Planning and Research Developmental Students Today I will talk about 1. Who are they? 2. What are some facts
More informationHypothesis Testing. Bluman Chapter 8
CHAPTER 8 Learning Objectives C H A P T E R E I G H T Hypothesis Testing 1 Outline 81 Steps in Traditional Method 82 z Test for a Mean 83 t Test for a Mean 84 z Test for a Proportion 85 2 Test for
More informationI. Basics of Hypothesis Testing
Introduction to Hypothesis Testing This deals with an issue highly similar to what we did in the previous chapter. In that chapter we used sample information to make inferences about the range of possibilities
More informationStatistics II (Discrete Methods: STAT 453/653) Fall 2007 Homework 5 Example Solution
Statistics II (Discrete Methods: STAT 453/653) Fall 007 Homework 5 Eamle Solution Written by: Fares Qeadan, Reviewed by: Ilya Zaliain Deartment of Mathematics Statistics, University of Nevada, Reno Problem
More informationSTATISTICS 8 CHAPTERS 1 TO 6, SAMPLE MULTIPLE CHOICE QUESTIONS
STATISTICS 8 CHAPTERS 1 TO 6, SAMPLE MULTIPLE CHOICE QUESTIONS Correct answers are in bold italics.. This scenario applies to Questions 1 and 2: A study was done to compare the lung capacity of coal miners
More informationEfficacy of Online Algebra I for Credit Recovery for AtRisk Ninth Graders: Consistency of Results from Two Cohorts
Efficacy of Online Algebra I for Credit Recovery for AtRisk Ninth Graders: Consistency of Results from Two Cohorts Authors and Affiliations: Jessica Heppen (AIR), Nicholas Sorensen (AIR), Elaine Allensworth
More informationAP STATISTICS (WarmUp Exercises)
AP STATISTICS (WarmUp Exercises) 1. Describe the distribution of ages in a city: 2. Graph a box plot on your calculator for the following test scores: {90, 80, 96, 54, 80, 95, 100, 75, 87, 62, 65, 85,
More informationNovember 24, Final Summary of the Summer Transition Grant Program
November 24, 2015 Final Summary of the Summer Transition Grant Program Authors Michael Mataczynski Research Intern Tel: 6512593917 Michael.Mataczynski@state.mn.us Nichole Sorenson Research Analyst Tel:
More informationE205 Final: Version B
Name: Class: Date: E205 Final: Version B Multiple Choice Identify the choice that best completes the statement or answers the question. 1. The owner of a local nightclub has recently surveyed a random
More informationRESULTS FROM HIGH SCHOOL EXIT SURVEYS 5/6/2015 SYSTEM PLANNING AND PERFORMANCE PORTLAND PUBLIC SCHOOLS HIGHLIGHTS
RESULTS FROM HIGH SCHOOL EXIT SURVEYS 5/6/2015 HIGHLIGHTS The majority of PPS high school seniors plan to graduate and to enroll in some type of higher education. Historically underserved racial groups
More informationThe predictability of security returns with simple technical trading rules
Journal of Emirical Finance 5 1998 347 359 The redictability of security returns with simle technical trading rules Ramazan Gençay Deartment of Economics, UniÕersity of Windsor, 401 Sunset, Windsor, Ont.,
More informationMultiperiod Portfolio Optimization with General Transaction Costs
Multieriod Portfolio Otimization with General Transaction Costs Victor DeMiguel Deartment of Management Science and Oerations, London Business School, London NW1 4SA, UK, avmiguel@london.edu Xiaoling Mei
More informationASSESSMENT AND PLACEMENT POLICIES Los Angeles Southwest College
ASSESSMENT AND PLACEMENT POLICIES Los Angeles Southwest College This report is part of a series of summaries that outlines the assessment and placement policies used across the nine community colleges
More informationTEXAS ENGINEERING FOUNDATION 20152016 Scholarship Application for Graduating Texas High School Seniors
TEXAS ENGINEERING FOUNDATION 20152016 Scholarship Application for Graduating Texas High School Seniors Application deadline is January 8, 2016 The chapters of the Texas Society of Professional Engineers
More informationNew Jersey Center for Teaching and Learning (CTL) 2015 Alumni Survey Results
New Jersey Center for Teaching and Learning (CTL) 2015 Alumni Survey Results This report summarizes the results of CTL s 2015 Teacher Endorsement Alumni Survey. CTL conducted this survey to determine the
More informationMethods for Estimating Kidney Disease Stage Transition Probabilities Using Electronic Medical Records
(Generating Evidence & Methods to imrove atient outcomes) Volume 1 Issue 3 Methods for CER, PCOR, and QI Using EHR Data in a Learning Health System Article 6 1212013 Methods for Estimating Kidney Disease
More informationComplex Conjugation and Polynomial Factorization
Comlex Conjugation and Polynomial Factorization Dave L. Renfro Summer 2004 Central Michigan University I. The Remainder Theorem Let P (x) be a olynomial with comlex coe cients 1 and r be a comlex number.
More informationLogistic and Poisson Regression: Modeling Binary and Count Data. Statistics Workshop Mark Seiss, Dept. of Statistics
Logistic and Poisson Regression: Modeling Binary and Count Data Statistics Workshop Mark Seiss, Dept. of Statistics March 3, 2009 Presentation Outline 1. Introduction to Generalized Linear Models 2. Binary
More informationPassing When It Counts Math courses present barriers to student success in California Community Colleges
Engaging Californians on Key Education Challenges I S S U E B R I E F F E B U A R Y 2 0 1 2 Passing When It Counts Math courses present barriers to student success in California Community Colleges Overview
More informationPlease complete this form and send it in with your completed Application Form and supporting credentials. Signature Date
Superintendent Letter of Eligibility Summary of Submission Please complete this form and send it in with your completed Application Form and supporting credentials. 1. Application for Admission 2. Three
More information