Institutional Assessment Report Texas Southern University College of Pharmacy and Health Sciences "P1-Aggregate Analyses of 6 cohorts ( )

Similar documents
Chapter 15. Time Series: Descriptive Analyses, Models, and Forecasting

Nature Neuroscience: doi: /nn Supplementary Figure 1. Spike-count autocorrelations in time.

Innova Junior College H2 Mathematics JC2 Preliminary Examinations Paper 2 Solutions 0 (*)

Licenciatura de ADE y Licenciatura conjunta Derecho y ADE. Hoja de ejercicios 2 PARTE A

R t. C t P t. + u t. C t = αp t + βr t + v t. + β + w t

Time series Decomposition method

Wednesday, November 7 Handout: Heteroskedasticity

Solutions to Odd Number Exercises in Chapter 6

Comparing Means: t-tests for One Sample & Two Related Samples

ACE 562 Fall Lecture 8: The Simple Linear Regression Model: R 2, Reporting the Results and Prediction. by Professor Scott H.

Math 10B: Mock Mid II. April 13, 2016

Vectorautoregressive Model and Cointegration Analysis. Time Series Analysis Dr. Sevtap Kestel 1

Outline. lse-logo. Outline. Outline. 1 Wald Test. 2 The Likelihood Ratio Test. 3 Lagrange Multiplier Tests

Financial Econometrics Jeffrey R. Russell Midterm Winter 2009 SOLUTIONS

Econ107 Applied Econometrics Topic 7: Multicollinearity (Studenmund, Chapter 8)

Measurement Error 1: Consequences Page 1. Definitions. For two variables, X and Y, the following hold: Expectation, or Mean, of X.

Econ Autocorrelation. Sanjaya DeSilva

Solutions: Wednesday, November 14

Area1 Scaled Score (NAPLEX) .535 ** **.000 N. Sig. (2-tailed)

Lecture 5. Time series: ECM. Bernardina Algieri Department Economics, Statistics and Finance

(10) (a) Derive and plot the spectrum of y. Discuss how the seasonality in the process is evident in spectrum.

Dynamic Econometric Models: Y t = + 0 X t + 1 X t X t k X t-k + e t. A. Autoregressive Model:

20. Applications of the Genetic-Drift Model

Vehicle Arrival Models : Headway

Version abc. General Certificate of Education. Mathematics MS2B Statistics 2. Mark Scheme examination - January series

CHEAPEST PMT ONLINE TEST SERIES AIIMS/NEET TOPPER PREPARE QUESTIONS

ESTIMATION OF DYNAMIC PANEL DATA MODELS WHEN REGRESSION COEFFICIENTS AND INDIVIDUAL EFFECTS ARE TIME-VARYING

Wednesday, December 5 Handout: Panel Data and Unobservable Variables

Department of Economics East Carolina University Greenville, NC Phone: Fax:

Nonlinearity Test on Time Series Data

Regression with Time Series Data

04. Kinetics of a second order reaction

Stationary Time Series

HYPOTHESIS TESTING. four steps. 1. State the hypothesis. 2. Set the criterion for rejecting. 3. Compute the test statistics. 4. Interpret the results.

ACE 564 Spring Lecture 7. Extensions of The Multiple Regression Model: Dummy Independent Variables. by Professor Scott H.

How to Deal with Structural Breaks in Practical Cointegration Analysis

Chickens vs. Eggs: Replicating Thurman and Fisher (1988) by Arianto A. Patunru Department of Economics, University of Indonesia 2004

Methodology. -ratios are biased and that the appropriate critical values have to be increased by an amount. that depends on the sample size.

HYPOTHESIS TESTING. four steps. 1. State the hypothesis and the criterion. 2. Compute the test statistic. 3. Compute the p-value. 4.

Testing the Random Walk Model. i.i.d. ( ) r

Chapter 16. Regression with Time Series Data

Diebold, Chapter 7. Francis X. Diebold, Elements of Forecasting, 4th Edition (Mason, Ohio: Cengage Learning, 2006). Chapter 7. Characterizing Cycles

Cointegration and Implications for Forecasting

Kriging Models Predicting Atrazine Concentrations in Surface Water Draining Agricultural Watersheds

Lecture 2 April 04, 2018

4.1 Other Interpretations of Ridge Regression

OBJECTIVES OF TIME SERIES ANALYSIS

Chapter 5. Heterocedastic Models. Introduction to time series (2008) 1

2.3 SCHRÖDINGER AND HEISENBERG REPRESENTATIONS

The Multiple Regression Model: Hypothesis Tests and the Use of Nonsample Information

Granger Causality Among Pre-Crisis East Asian Exchange Rates. (Running Title: Granger Causality Among Pre-Crisis East Asian Exchange Rates)

Tourism forecasting using conditional volatility models

A complementary test for ADF test with an application to the exchange rates returns

Modeling and Forecasting Volatility Autoregressive Conditional Heteroskedasticity Models. Economic Forecasting Anthony Tay Slide 1

Lecture 4. Classical Linear Regression Model: Overview

ECON 482 / WH Hong Time Series Data Analysis 1. The Nature of Time Series Data. Example of time series data (inflation and unemployment rates)

CHAPTER 9. Exercise Solutions

Solutions to Exercises in Chapter 12

Properties of Autocorrelated Processes Economics 30331

A multivariate labour market model in the Czech Republic 1. Jana Hanclová Faculty of Economics, VŠB-Technical University Ostrava

Elements of Stochastic Processes Lecture II Hamid R. Rabiee

Hypothesis Testing in the Classical Normal Linear Regression Model. 1. Components of Hypothesis Tests

22. Inbreeding. related measures: = coefficient of kinship, a measure of relatedness of individuals of a population; panmictic index, P = 1 F;

Mean Reversion of Balance of Payments GEvidence from Sequential Trend Break Unit Root Tests. Abstract

Answers to Exercises in Chapter 7 - Correlation Functions

CHAPTER 2: Mathematics for Microeconomics

A Specification Test for Linear Dynamic Stochastic General Equilibrium Models

The Simple Linear Regression Model: Reporting the Results and Choosing the Functional Form

GDP PER CAPITA IN EUROPE: TIME TRENDS AND PERSISTENCE

The General Linear Test in the Ridge Regression

You must fully interpret your results. There is a relationship doesn t cut it. Use the text and, especially, the SPSS Manual for guidance.

Asymptotic Equipartition Property - Seminar 3, part 1

What Ties Return Volatilities to Price Valuations and Fundamentals? On-Line Appendix

Impact of International Information Technology Transfer on National Productivity. Online Supplement

ACE 562 Fall Lecture 4: Simple Linear Regression Model: Specification and Estimation. by Professor Scott H. Irwin

Summer Term Albert-Ludwigs-Universität Freiburg Empirische Forschung und Okonometrie. Time Series Analysis

Testing H 0 : ρ = 0: Comparing A Single Correlation to Zero

2.4 Cuk converter example

Group B Human

DEPARTMENT OF ECONOMICS

14 Autoregressive Moving Average Models

Distribution of Least Squares

Navneet Saini, Mayank Goyal, Vishal Bansal (2013); Term Project AML310; Indian Institute of Technology Delhi

A note on spurious regressions between stationary series

Distribution of Estimates

Stock Market Development and Economic Growth: War and Post War Evidence from Sri Lanka

CALENDAR ANOMALIES AND CAPITAL MARKET EFFICIENCY: LISTED PROPERTY TRUST INVESTMENT STRATEGIES

Forecasting optimally

The Effect of Nonzero Autocorrelation Coefficients on the Distributions of Durbin-Watson Test Estimator: Three Autoregressive Models

Unit Root Time Series. Univariate random walk

Testing for a Single Factor Model in the Multivariate State Space Framework

An Introduction to Malliavin calculus and its applications

STRUCTURAL CHANGE IN TIME SERIES OF THE EXCHANGE RATES BETWEEN YEN-DOLLAR AND YEN-EURO IN

The Validity of the Tourism-Led Growth Hypothesis for Thailand

Ready for euro? Empirical study of the actual monetary policy independence in Poland VECM modelling

in Engineering Prof. Dr. Michael Havbro Faber ETH Zurich, Switzerland Swiss Federal Institute of Technology

STAD57 Time Series Analysis. Lecture 17

Linear Combinations of Volatility Forecasts for the WIG20 and Polish Exchange Rates

15. Which Rule for Monetary Policy?

STAD57 Time Series Analysis. Lecture 17

Transcription:

Insiuional Assessmen Repor Texas Souhern Universiy College of Pharmacy and Healh Sciences "P1-Aggregae Analyses of 6 cohors (2009-14) The following analysis illusraes relaionships beween PCAT Composie Scores, Pre-Pharm OVGPA, Pre-Pharm Science GPA, PODA I Final Grd., Calculaions Final Grd., Firs Sem. GPA. Based on aggregae analyses [coefficien equaions] of he 6 cohors, he repor includes recommended [admissions crieria] hresholds of each variable. Correlaions 1s Sem. [Fall] GPA Calculaions To. Ps. PODA I To. Ps. SGPA Pearson Correlaion.207 **.123 **.149 ** (2-ailed).000.003.000 N 595 600 608 PCAT Composie Pearson Correlaion.319 **.403 **.300 ** (2-ailed).000.000.000 N 594 599 607 OVGPA Pearson Correlaion.216 **.139 **.171 ** (2-ailed).000.001.000 N 595 600 608 **. Correlaion is significan a he 0.01 level (2-ailed). All independen/dependen variables 2-ailed levels are <.05, which indicaes ha he correlaions among independen/dependen variables are saisically significan. The relaionships are all posiive (12-40% respecively), showing moderae o srong relaionships among all variables.

1) a Unsandardized 1 (Consan) 3.001.077 38.917.000 1s Sem. [Fall] GPA.126.024.207 5.164.000 a. Dependen Variable: SGPA equaions sugges, SGPA required for min. (2.5) 1 s Sem. GPA = 3.316 2) a Unsandardized 1 (Consan) 3.305.032 101.828.000 Calculaions To. Ps..022.007.123 3.021.003 a. Dependen Variable: SGPA equaions sugges, SGPA required for min. (3.0 - C) CAL. grade = 3.371 3) a Unsandardized 1 (Consan) 3.272.036 91.071.000 PODA I To. Ps..034.009.149 3.712.000 a. Dependen Variable: SGPA equaions sugges, SGPA required for min. (3.0 - C) PODA I grade = 3.374 4) a Unsandardized 1 (Consan) 3.320.155 21.376.000 NAPLEX Toal Scaled Score.001.002.047.586.559 a. Dependen Variable: SGPA equaions sugges, SGPA required for min. (75) NAPLEX score = 3.395

5) a Unsandardized 1 (Consan) 13.180 4.178 3.154.002 1s Sem. [Fall] GPA 10.852 1.326.319 8.183.000 a. Dependen Variable: Composie equaions sugges, PCAT composie required for min. (2.5) 1 s Sem. GPA = 40.31 6) a Unsandardized 1 (Consan) 30.530 1.670 18.278.000 Calculaions To. Ps. 3.996.372.403 10.749.000 a. Dependen Variable: PCAT Composie equaions sugges, PCAT composie required for min. (3.0 - C) CAL. grade = 42.51 7) a Unsandardized 1 (Consan) 32.715 1.939 16.870.000 PODA I To. Ps. 3.872.501.300 7.730.000 a. Dependen Variable: PCAT Composie equaions sugges, PCAT composie required for min. (3.0 - C) PODA I grade = 44. 8) a Unsandardized 1 (Consan) 30.137 7.837 3.845.000 NAPLEX Toal Scaled Score.179.079.177 2.259.025 a. Dependen Variable: PCAT Composie equaions sugges, PCAT composie required for min. (75) NAPLEX score = 43.56

9) a Unsandardized 1 (Consan) 3.054.066 46.593.000 1s Sem. [Fall] GPA.112.021.216 5.400.025 a. Dependen Variable: OVGPA equaions sugges, OVGPA required for min. (2.5) 1 s Sem. GPA = 3.334 10) a Unsandardized 1 (Consan) 3.317.028 120.128.000 Calculaions To. Ps..021.006.139 3.421.001 a. Dependen Variable: OVGPA equaions sugges, OVGPA required for min. (3.0 - C) CAL. grade = 3.380 11) a Unsandardized 1 (Consan) 3.282.031 107.571.000 PODA I To. Ps..034.008.171 4.273.000 a. Dependen Variable: OVGPA equaions sugges, OVGPA required for min. (3.0 - C) PODA grade = 3.384 12) a Unsandardized 1 (Consan) 3.349.121 27.675.000 NAPLEX Toal Scaled Score.001.001.037.463.644 a. Dependen Variable: OVGPA equaions sugges, OVGPA required for min. (75) NAPLEX score = 3.424

Furher examinaions were conduced o explore saisically significan facors beween suden s who ve passed vs failed he NAPLEX examinaion. Also idenified were hose who had a risk facor associaed [i.e. ovgpa < 3.3; comp < 43; sgpa < 3.3]. 159 Toal NAPLEX paricipans (2013 & 2014) 144 (91%) Passed 91 wih risk facor(s) 53 w/o risk facor(s) 15 Failed (7 earned < C in PODA and/or Calculaions) 11 wih risk facors 4 w/o risk facors Variables in he Equaion B S.E. Wald df Exp(B) Sep 1 a @1sSem.FallGPA -1.222 1.310.870 1.351.295 CalculaionsTo.Ps.113.249.207 1.649 1.120 PODAITo.Ps.745.313 5.661 1.017 2.106 Reading.019.043.201 1.654 1.020 Composie.005.136.002 1.969 1.005 Verbal -.032.040.639 1.424.968 Biology.036.038.924 1.337 1.037 QA.003.035.005 1.943 1.003 Chemisry -.011.042.068 1.795.989 OVGPA 1.384 2.138.419 1.517 3.991 SGPA -1.222 1.613.573 1.449.295 Consan 1.592 5.824.075 1.785 4.914 a. Variable(s) enered on sep 1: @1sSem.FallGPA, CalculaionsTo.Ps, PODAITo.Ps, Reading, Composie, Verbal, Biology, QA, Chemisry, OVGPA, SGPA. A binary regression was performed (above able) o furher idenify which variable significanly increases chances of passing NAPLEX. The es suggess ha a passing grade in PODA I increases he esimaed logi of passing he NAPLEX examinaion by.745 unis. The relaionship may also be expressed in erms of an odds raio [i.e. he exponenial of.745 is 2.106]. Thus, sudens who perform well in PODA I are 2.1 imes more likely o pass he NAPLEX examinaion on he firs aemp.

dimension1 Group Saisics NAPLEX Pass/Fail N Mean Sd. Deviaion Sd. Error Mean PODA I To. Ps. 1 144 3.760 1.2045.1004 0 15 2.933 1.4622.3775 Independen Samples Tes Levene's Tes for Equaliy of Variances -es for Equaliy of Means F df (2- ailed) Mean Difference Sd. Error Difference 95% Confidence Inerval of he Difference Lower Upper PODA I To. Ps. Equal variances assumed Equal variances no assumed.841.360 2.479 157.014.8271.3336.1681 1.4861 2.117 16.041.050.8271.3907 -.0009 1.6551 As depiced in he above ables, he homogeneiy of variance ess significance value is.360 (and no <.05) hus, we may be confiden of he inerpreaion of he -value (2.479) and is significance (.014). P<.05, herefore, we can rejec he null hypohesis of no saisically significan difference beween he mean PODA I grade of sudens who passed versus failed he NAPLEX examinaion; and conclude ha in all probabiliy he difference beween sudens who pass or fail PODA I on heir firs aemp is saisically significan in heir NAPLEX performance.