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112: Stat Lecture 7 Notes Homework 2: Due next Thursday The Multiple Linear Regression model (Chapter 4.1) Inferences from multiple regression analysis (Chapter 4.2) Interpretation of Regression Coefficients Gas mileage regression from Car89.JMP Response GP1000MHwy Parameter Estimates Term Intercept Weight(lb) Cargo Seating Horsepower Estimate 19.100521 0.0040877 0.0533 0.0268912 0.0426999 Std Error 2.098478 0.001203 0.013787 0.428283 0.01567 t Ratio Prob>|t| 9.10 <.0001 3.40 0.0010 3.87 0.0002 0.06 0.9501 2.73 0.0075 Interpretation of coefficient bweight = 0.004 : The mean of GP1000Mwy is estimated to increase 0.004 for a one pound increase in weight holding fixed cargo, seating and horsepower. E (Y | X 1 = x1 + 1, X 2 = x2 , , X K = xK ) E (Y | X 1 = x1 , , X K = xK ) = ( 0 + 1 ( x1 + 1) + + K xK ) ( 0 + 1 x1 + + K xK ) = 1 Partial Slopes vs. Marginal Slopes Multiple Linear Regression Model: E (Y | X 1 , , X K ) = 0 + 1 X 1 + + K X K The coefficient k is a partial slope. It indicates the change in the mean of y that is associated with a one unit increase in xk while holding all other variables x1 ,..., xk 1 , xk +1, ..., xK fixed. A marginal slope is obtained when we perform a simple regression with only one X, ignoring all other variables. Consequently the other variables are not held fixed. Partial vs. Marginal Slopes Example How much gas will it use to carry an additional 200 pound passenger for 1000 miles? Simple Regression Parameter Estimates Term Intercept Weight(lb) Estimate 18.278693 0.0067879 Std Error 1.872435 0.000655 t Ratio 9.76 10.36 Prob>|t| <.0001 <.0001 Multiple Regression Parameter Estimates Term Intercept Weight(lb) Cargo Seating Horsepower Estimate 19.100521 0.0040877 0.0533 0.0268912 0.0426999 Std Error 2.098478 0.001203 0.013787 0.428283 0.01567 t Ratio 9.10 3.40 3.87 0.06 2.73 Prob>|t| <.0001 0.0010 0.0002 0.9501 0.0075 Marginal slope suggest 0.0068*200=1.36 gallons Partial slope suggests 0.0041*200=0.82 gallons The partial slope better answers the question because when we add the additional passenger, we are planning to keep cargo, seating and horsepower constant. Partial Slopes vs. Marginal Slopes: Another Example In order to evaluate the benefits of a proposed irrigation scheme in a certain region, suppose that the relation of yield Y to rainfall R is investigated over several years. Data is in rainfall.JMP. Bivariate Fit of Yield By Total Spring Rainfall 90 80 70 Yield 60 50 40 30 7 8 9 10 11 12 13 Total Spring Rainfall Linear Fit Linear Fit Yield = 76.666667 - 1.6666667 Total Spring Rainfall Summary of Fit RSquare RSquare Adj Root Mean Square Error Mean of Response Observations (or Sum Wgts) 0.027778 -0.13426 13.94433 60 8 Parameter Estimates Term Intercept Total Spring Rainfall Estimate 76.666667 -1.666667 Std Error 40.5546 4.025382 t Ratio 1.89 -0.41 Prob>|t| 0.1076 0.6932 Bivariate Fit of Average Spring Temperature By Total Spring Rainfall 57.5 Average Spring Temperature 55 52.5 50 47.5 45 42.5 7 8 9 10 11 12 13 Total Spring Rainfall Higher rainfall is associated with lower temperature. Multiple Linear Regression Response Yield Parameter Estimates Term Intercept Total Spring Rainfall Average Spring Temperature Estimate -144.7619 5.7142857 2.952381 Std Error 55.8499 2.680238 0.692034 t Ratio -2.59 2.13 4.27 Prob>|t| 0.0487 0.0862 0.0080 Rainfall is estimated to be beneficial once temperature is held fixed. Multiple regression provides a better picture of the benefits of an irrigation scheme because temperature would be held fixed in an irrigation scheme. Inferences about Regression Coefficients Confidence intervals: (1 )100% confidence interval for k : bk t / 2 sb Degrees of freedom for t equals n-(K+1). Standard error of bk , sb , found on JMP output. Hypothesis Test: H 0 : k = k* k k Decision rule for test: Reject H0 if t > t / 2 or t < t / 2 bk k* t= where sb p-value for testing H 0 : k = 0 is printed in JMP output under Prob>|t|. k H a : k k* Inference Examples Find a 95% confidence interval for horsepower ? Is seating of any help in predicting gas mileage once horsepower, weight and cargo have been taken into account? Carry out a test at the 0.05 significance level. 95% Confidence interval for horsepower : bhorsepower t.025,n (4+1) * sbhorsepower = 0.0427 t.025,104 *0.01567 = 0.0427 1.98*0.01567 = (0.0117, 0.0737) Is seating of any help in predicting gas mileage once horsepower, and weight cargo have been taken into account? H 0 : seating = 0 H a : seating 0 t= bseating 0 sbseating = 0.0269 0 = 0.0628 0.4283 We reject H 0 if | t |> t.025,n ( K +1) = 1.98 Thus, we do not reject H 0 ; there is no Evidence that seating is of any help in Predicting as mileage once horsepower, weight and cargo have been taken into account. Checking Assumptions: Multiple Linear Regression Model E (Y | x1 , , xK )(= y| x1 ,..., xK ) = 0 + 1 x1 + + K xK yi = 0 + 1 x1i + 2 x2i + + K xKi + ei The expected value of the disturbances is zero for each ( x1 , , xK ) , E (ei | xi1 , , xiK ) = 0 2 ei e ,i.e., The variance of each is equal to Var (ei | xi1 , , xiK ) = e2 The ei are normally distributed. The ei are independent. Plots for Checking Assumptions We can construct residual plots of each explanatory variable Xk vs. the residuals. We save the residuals by clicking the red triangle next to Response after fitting the model and clicking Save Columns and then residuals. We then plot Xk vs. the residuals using Fit Y by X (where Y=the residuals). We can plot a horizontal line at 0 by using Fit Y by X (it is a property of multiple linear regression that the least squares line for the regression of the residuals on any Xk is a horizontal line. A useful summary of the residual plots for each explanatory variable is the Residual by Predicted plot that is automatically plotted after using Fit Model. The residual ,by, predicted plot is a plot of the predicted values E (Y | X i1 X iK ) , , vs. the residuals Checking Assumptions Linearity: Check that in residual by predicted plot, the mean of the residuals for each range of the predicted values is about zero. Check that in each residual plot, the mean of the residuals for each range of the explanatory variable is about zero. Constant Variance: Check that in the residual by predicted plot that for each range of the predicted values, the spread of the residuals is about the same. Normality: Plot histogram of the residuals. Check that the histogram is bell shaped. 10 GP1000MHwy Residual 5 Bivariate Fit of Residual GP1000MHwy By Horsepower 10 Residual GP1000MHwy 0 -5 -10 25 30 35 40 45 50 55 GP1000MHwy Predicted 5 0 Residual by predicted plot does not suggest and suggests approximately constant variance 10 Residual GP1000MHwy 5 -5 -10 50 100 150 Horsepower 200 250 0 Plot of horsepower vs. residuals suggests linearity is okay. -5 -10 1500 2000 2500 3000 3500 4000 Weight(lb) Plot of weight vs. residuals suggests linearity is okay. One potential concern is that highest weight cars all have negative residuals. Bivariate Fit of Residual GP1000MHwy By Seating 10 Residual GP1000MHwy Bivariate Fit of Residual GP1000MHwy By Horsepower 10 Residual GP1000MHwy 2 3 4 5 Seating 6 7 8 5 5 0 0 -5 -5 -10 -10 50 100 150 Horsepower 200 250 Plot of residuals vs. seating suggests linearity is not perfect for seating. Residuals for small and high seating seem to have a mean that is smaller than 0. Plot of residuals vs. horsepower suggest linearity is okay. Highest 4 horsepower cars all have negative residuals but next 5 highest horsepower cars all have positive residuals. Coefficient of Determination The coefficient of determination R 2 for multiple regression is defined as for simple linear regression: R 2 = SSR = 1 SSE SST SST Represents percentage of variation in y that is explained by the multiple regression line. R2 is between 0 and 1. The closer to 1, the better the fit of the regression equation to the data. Assessing Quality of Prediction (Chapter 3.5.3) R squared measures is a measure of a fit of the regression to the sample data. It is not generally considered an adequate measure of the regression s ability to predict the responses for new observations. One method of assessing the ability of the regression to predict the responses for new observations is data splitting. We split the data into a two groups a training sample and a holdout sample (also called a validation sample). We fit the regression model to the training sample and then assess the quality of predictions of the regression model to the holdout sample. College Data in collegeclass.JMP Training Sample: 40 observations. Holdout Sample: Last 10 observations. Mean Squared Deviation: Mean squared prediction error over the holdout sample (y y ) over the n2 (=10 here) observations n2 2 i =1 i i in the holdout sample. n2
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Path: UPenn >> STAT >> 112 Fall, 2008
Description: Stat 112: Lecture 8 Notes Homework 2: Due on Thursday Assessing Quality of Prediction (Chapter 3.5.3) Comparing Two Regression Models (Chapter 4.4) Prediction Intervals for Multiple Regression (Chapter 4.5) Assessing Quality of Prediction (Chapt...
notes6.ppt
Path: UPenn >> STAT >> 112 Fall, 2008
Description: Lecture 6 Notes Note: I will e-mail homework 2 tonight. It will be due next Thursday. The Multiple Linear Regression model (Chapter 4.1) Inferences from multiple regression analysis (Chapter 4.2) In multiple regression analysis, we consider more ...
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Path: UPenn >> STAT >> 112 Fall, 2008
Description: Stat 112 Spring 2004 Midterm 1 Solutions 1. (i) a; (ii) b; (c) d; (iv) b; (v) a 2. Study II is most likely the randomized study. In a large randomized study, any variable will almost surely be closely balanced between the treatment and placebo group...
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Path: UPenn >> STAT >> 550 Fall, 2008
Description: Statistics 550 Notes 21 Reading: Section 4.2 I. Neyman-Pearson Lemma (Section 4.2) Define the likelihood ratio statistic by p ( X | 1 ) L( X , 0 , 1 ) = , p( X | 0 ) where p( x | ) is the probability mass function or probability density function of...
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Path: UPenn >> STAT >> 112 Fall, 2008
Description: Multiple Regression Practice Problems Solutions Stat 112 1. (a) Pennsylvanias predicted SAT score from the multiple regression model is 932.414 + 4.299*50 + (-3.074)*27.98 = 899.000. Pennsylvanias residual is 885-899.000= -14. (b) To test if the mu...
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Path: UPenn >> STAT >> 510 Fall, 2008
Description: Statistics 510: Notes 15 Reading: Sections 5.4-5.5 I. Uniform Random Variables A random variable is said to be uniformly distributed over the interval ( , ) if its pdf is given by 1 if < x < f ( x) = 0 otherwise Example: Suppose events over...
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Path: UPenn >> STAT >> 510 Fall, 2008
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Path: UPenn >> STAT >> 550 Fall, 2008
Description: Statistics 550 Notes 16 Schedule: 1. I will e-mail Homework 7 to you tonight. 2. I will return your midterms on Tuesday. 3. Homework 6 is due on Friday. Reading: Section 3.2 I. Computation of Bayes Procedures Review The Bayes risk of a decision proce...
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Path: UPenn >> STAT >> 475 Fall, 2009
Description: Statistics 475 Notes 15 Reading: Lohr, Chapters 6.1-6.2 Rubens question from last class: In planning a two stage cluster design, what happens if the cluster size M is large but unknown? Suppose the cost is c1 and c2 for sampling a unit within a clust...
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Path: UPenn >> STAT >> 550 Fall, 2008
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Path: UPenn >> STAT >> 475 Fall, 2009
Description: Stat 475/920 Notes 1 Reading: Lohr, Chapter 1.1-1.4, 1.6 I. Survey Sampling Goals and Basic Terminology Sample surveys are used to obtain information about a population by examining only a small fraction of the population. Examples of fields in which...
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Path: UPenn >> STAT >> 510 Fall, 2008
Description: Statistics 510: Notes 10 Reading: Sections 4.6-4.7 I. Review on Variance The variance of a random variable measures its typical dispersion about its expected value. The variance of a random variable X is defined by Var ( X ) = E[( X E{ X }) 2 ] = E ...
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Path: UPenn >> STAT >> 112 Fall, 2008
Description: Homework 1, Statistics 112, Fall 2005 This homework is due Thursday, September 21st at the beginning of class. Notes: All data sets mentioned can be found under the data sets link on our web site. 1. One general belief held by observers of the busine...
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Path: UPenn >> STAT >> 550 Fall, 2008
Description: Stat 550 Notes 17 Reading: Chapter 4.3 I. Uniformly Most Powerful Tests When the alternative hypothesis is composite, H1 : 1 , then the power can be different for different alternatives. For each particular alternative 1 , a test is the most powerf...
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Path: UPenn >> STAT >> 550 Fall, 2008
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Path: UPenn >> STAT >> 550 Fall, 2008
Description: Statistics 550 Notes 4 Reading: Sections 1.4, 1.5 Note: For Thursday, Sept. 18th, I will hold my office hours from 1-2 instead of my usual time 4:45-5:45. I. Prediction (Chapter 1.4) A common decision problem is that we want to predict a variable Y b...
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Path: UPenn >> STAT >> 112 Fall, 2008
Description: Homework 2, Statistics 112, Fall 2005 This homework is due Thursday, October 5th at the beginning of class. Notes: All data sets mentioned can be found under the data sets link on our web site. 1. (a) Problem 3.37 in our textbook, Applied Regression ...
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Path: UPenn >> STAT >> 112 Fall, 2008
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Path: UPenn >> STAT >> 112 Fall, 2008
Description: Homework 5, Statistics 112, Fall 2004 This homework is due Tuesday, October 19th at the beginning of class. 1. In most jurisdictions, driving an automobile with a blood alcohol level in excess of .08 is a felony. Because of a number of factors, it is...
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Path: UPenn >> SAS >> 535 Fall, 2009
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Path: UPenn >> RS >> 535 Fall, 2008
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Path: UPenn >> TORONTO >> 1 Fall, 2009
Description: Presentations at Univeristy of Toronto for 11 and 12 April 2007 \"Pursuing the Para-Scriptural by way of the Pre-Scriptural\" [Seminar, 4 pm on Wednesday, 11 April 2007] Introductory o assumption - source producers are being honest (even if wrong!), a...
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Path: UPenn >> NEW >> 2 Fall, 2009
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Path: UPenn >> MUC >> 7 Fall, 2009
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Path: UPenn >> I >> 05 Fall, 2009
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Path: UPenn >> C >> 80 Fall, 2009
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Path: UPenn >> C >> 88 Fall, 2009
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Path: UPenn >> M >> 95 Fall, 2009
Description: SRA : Description of the SRA System as Used for MUC 6 George R. Krupka Systems Research and Applications 4300 Fair Lakes Court South Building, Suite 50 0 Fairfax, VA 22033423 2 krupka@sra.com INTRODUCTIO N SRA used the combination of two systems ...
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Path: UPenn >> M >> 93 Fall, 2009
Description: NEC : DESCRIPTION OF THE VENIEX SYSTE M AS USED FOR MUC- 5 Kazunori MURAKI, Shinichi DOI and Shinichi AND O NEC Corp. Information Technology Research Laboratorie s Human Language Research Laborator y 411, Miyazaki, Miyamae-ku, Kawasaki 216, JAPA N E...
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Path: UPenn >> M >> 95 Fall, 2009
Description: UNIVERSITY OF DURHAM : DESCRIPTION OF THE LOLITA SYSTEM AS USED IN MUC-6 . Richard Morgan, Roberto Garigliano , Paul Callaghan, Sanjay Poria, Mark Smith, Agnieszka Urbanowicz , Russell Collingham, Marco Costantino, Chris Cooper, and the LOLITA Group ...
M95-1013.pdf
Path: UPenn >> M >> 95 Fall, 2009
Description: CRL/NMSU Description of the CRL/NMSU Systems Used for MUC- 6 Jim Cowie Computing Research Laboratory New Mexico State Universit y jcowie@nmsu.edu (505) 646-5181 Introductio n This paper discusses the two CRL named entity recognition systems submitted...
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Path: UPenn >> X >> 96 Fall, 2009
Description: N T T D A T A : D E S C R I P T I O N OF T H E E R I E S Y S T E M USED FOR MUC-6 Yoshio Eriguchi and Tsuyoshi Kitani NTT Data Communications Systems Corporation K o w a K a w a s a k i Nishi-guchi Bldg., 66-2 Horikawa-cho, Saiwai-ku, Kawasaki-shi, K...
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Path: UPenn >> M >> 91 Fall, 2009
Description: SYNCHRONETICS : DESCRIPTION OF THE SYNCHRONETICS SYSTE M USED FOR MUC- 3 James Mayfield Edwin Addison Computer Science Dept . University of Maryland, Baltimore Count y Baltimore, MD 21228-539 8 mayfield0umbc3 .umbc.edu (301) 455-3099 Synchronetics,...
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Path: UPenn >> M >> 91 Fall, 2009
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Path: UPenn >> M >> 92 Fall, 2009
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Path: UPenn >> M >> 93 Fall, 2009
Description: TRW: DESCRIPTION OF THE DEFT SYSTEM AS USED FOR MUC- 5 WILLIAM W. NOAH, Ph.D. ROLLIN V. WEEKS TRW SYSTEMS DEVELOPMENT DIVISION ONE SPACE PARK REDONDO BEACH, CA 9027 8 R2/218 6 BACKGROUN D For the past three years, TRW has been developing a text analy...
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Path: UPenn >> M >> 91 Fall, 2009
Description: GTE: DESCRIPTION OF THE TIA SYSTEM USED FOR MUC- 3 Robert Dietz GTE Government Systems Corporatio n 100 Ferguson Driv e Mountain View, CA 9403 9 dietz%gtewd .dnet@gte .com (415) 966-282 5 INTRODUCTIO N This paper describes the version of GTE\'s Text I...
M93-1024.pdf
Path: UPenn >> M >> 93 Fall, 2009
Description: Description of the LINK System Used for MUC- 5 Steven L . Lytinen, Robert R . Burridge, Peter M. Hastings, and Christian Huyck Artificial Intelligence Laboratory The University of Michigan Ann Arbor, MI 4810 9 E-mail: lytinen Oengin .umich .edu BAC...
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Path: UPenn >> M >> 93 Fall, 2009
Description: SRA: DESCRIPTION OF THE SOLOMON SYSTEM AS USED FOR. MUC- 5 Chinatsu Aone, Sharon Flank, Doug McKee, Paul Kraus e Systems Research and Applications (SRA ) 2000 15th Street Nort h Arlington, VA 2220 1 aonecQ sra.coin BACKGROUN D SRA used a language-in...
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Path: UPenn >> M >> 93 Fall, 2009
Description: BBN : Description of the PLUM System as Used for MUC- 5 The PLUM System Group * BBN Systems and Technologies 70 Fawcett Stree t Cambridge, MA 0213 8 weischedel@bbn .com APPROACH Traditional approaches to the problem of extracting data from texts have...
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Path: UPenn >> M >> 92 Fall, 2009
Description: HUGHES RESEARCH LABORATORIES : DESCRIPTION OF THE TRAINABLE TEXT SKIMMER USED FOR MUC-4 Stephanie E . August Charles P . Dola n Hughes Aircraft Company Electro-Optical and Data Systems Group P.O. Box 902 - EO E52 C235 El Segundo, CA 90245-0902 au...
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Path: UPenn >> M >> 92 Fall, 2009
Description: NEW YORK UNIVERSITY DESCRIPTION OF THE PROTEUS SYSTEM AS USED FOR MUC- 4 Ralph Grishman, Catherine Macleod, and John Sterling , The PROTEUS Project Computer Science Departmen t New York University 715 Broadway, 7th Floor New York, NY 1000 3 { grishma...
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Path: UPenn >> M >> 92 Fall, 2009
Description: USC : Description of the SNAP System Used for MUC- 4 D. Moldovan, S . Chet, M. Chung, K. Hendrickson, J. Kim, and S . Kowalski Parallel Knowledge Processing Laborator y University of Southern Californi a Los Angeles, California 90089-2562 moldovan...
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Path: UPenn >> MUC >> 7 Fall, 2009
Description: 1 .ksat siht no ecnamrofrep gnicnahne rof ygetarts evitcee na detartsnomed evah ew taht eveileb ew tub ,ytinummoc eht yb devlos eb ot tey smelborp latnemadnuf stneserp llits ,tsartnoc yb ,TS .level taht ta tsomla saw RT elihw ,level ecnamrofrep lano...
sat422.pdf
Path: UPenn >> WISE >> 2004 Fall, 2009
Description: Internet Exchanges for Used Goods Michael D. Smith, Rahul Telang H. John Heinz III School of Public Policy and Management, Carnegie Mellon University Extended Abstract for Consideration in WISE 2004 The emergence of simultaneous on-line markets for u...
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Path: UPenn >> ECON >> 2 Fall, 2008
Description: The most commonly-used money definition in the U.S. is M2 M1 includes Cash (fiat money) travelers checks checking deposits held by private individuals and businesses very liquid M2 includes M1 plus savings deposits time deposits valued under ...
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Path: UPenn >> CIT >> 597 Fall, 2009
Description: DTDs Document Type Definitions Apr 10, 2009 Schemas XML documents are designed to be processed by computer programs If you can put just any tags in an XML document, in any order, its very hard to write a program that knows how to process the ta...
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Path: UPenn >> CIT >> 597 Fall, 2009
Description: DTDs Apr 10, 2009 XML and DTDs A DTD (Document Type Definition) describes the structure of one or more XML documents. Specifically, a DTD describes: Elements Attributes, and Entities (We will discuss each of these in turn) An XML docume...
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Path: UPenn >> CIT >> 597 Fall, 2009
Description: SQL Apr 10, 2009 SQL SQL is Structured Query Language Some people pronounce SQL as sequel Other people insist that only ess-cue-ell is the only correct pronunciation SQL is an ANSI (American National Standards Institute) standard Most also ext...
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Path: UPenn >> CIT >> 597 Fall, 2009
Description: XML Apr 10, 2009 HTML and XML, I XML stands for eXtensible Markup Language HTML is used to mark up text so it can be displayed to users HTML describes both structure (e.g. <p>, <h2>, <em>) and appearance (e.g. <br>, <font>,<i>) HTML uses a fixed, u...
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Path: UPenn >> CIT >> 597 Fall, 2009
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Path: UPenn >> CIT >> 590 Fall, 2009
Description: Classes and Methods Apr 10, 2009 Objects have behaviors In old style programming, you had: data, which was completely passive functions, which could manipulate any data In O-O programming, an object contains both data and methods that manip...
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Path: UPenn >> CIT >> 597 Fall, 2009
Description: Clients and Servers Apr 10, 2009 URL review A URL has the syntax: protocol:/hostname:port/path#anchor importjava.net.*; This is the package that defines sockets, URLs, etc. Constructs a URL object from a text string This exception is thrown ...
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Path: UPenn >> CIT >> 591 Fall, 2009
Description: Starting Classes and Methods Objects have behaviors In old style programming, you had: data, which was completely passive functions, which could manipulate any data In O-O programming, an object contains both data and methods that manipulate th...
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Path: UPenn >> CIT >> 597 Fall, 2009
Description: SQL Apr 10, 2009 SQL SQL is Structured Query Language Some people pronounce SQL as sequel Other people insist that only ess-cue-ell is the only correct pronunciation SQL is an ANSI (American National Standards Institute) standard Most also ext...
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Path: UPenn >> CIT >> 591 Fall, 2009
Description: Introduction to Primitives Apr 10, 2009 Overview Today we will discuss: The eight primitive types, especially int and double Declaring the types of variables Operations on primitives The assignment statement How to print results Primitive...
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Path: UPenn >> CIT >> 597 Fall, 2009
Description: Simple UML Apr 10, 2009 What is UML? UML stands for Unified Modeling Language UML is a diagramming language designed for ObjectOriented programming UML can be used to describe: the organization of a program how a program executes how a p...
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Path: UPenn >> CIT >> 597 Fall, 2009
Description: MVC for Servlets Apr 10, 2009 MVC One of the most common Design Patterns is ModelView-Controller (MVC) The model does all the computational work It is input/output free All communication with the model is via methods User input goes to the c...
38-uml.ppt
Path: UPenn >> CIT >> 597 Fall, 2009
Description: Simple UML Apr 10, 2009 What is UML? UML stands for Unified Modeling Language UML is a diagramming language designed for ObjectOriented programming UML can be used to describe: the organization of a program how a program executes how a p...
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Path: UPenn >> CIT >> 594 Fall, 2009
Description: Types of Algorithms Algorithm classification Algorithms that use a similar problem-solving approach can be grouped together This classification scheme is neither exhaustive nor disjoint The purpose is not to be able to classify an algorithm as on...
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Path: UPenn >> CIT >> 597 Fall, 2009
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Path: UPenn >> CIT >> 597 Fall, 2009
Description: CSS Applications to HTML and XHTML Apr 10, 2009 The problem with HTML HTML was originally intended to describe the content of a document Page authors didnt have to describe the layout-the browser would take care of that This is a good enginee...
0readme.txt
Path: UPenn >> LDC >> 2006 Fall, 2009
Description: README File for the FRENCH GIGAWORD TEXT CORPUS = First Edition = INTRODUCTION - French Gigaword is a comprehensive archive of newswire text data that has been acquired over several year...
README.pdf
Path: UPenn >> LDC >> 2008 Fall, 2008
Description: THE WEST POINT BRAZILIAN PORTUGUESE SPEECH CORPUS The Center For Technology Enhanced Language Learning United States Military Academy Department Of Foreign Languages 745 Brewerton Road West Point, NY 10996 Email: john.morgan@usma.edu Phone: 845-938-6...
transpec_iraqi.doc
Path: UPenn >> LDC >> 2006 Fall, 2009
Description: Appen Iraqi Transcription Conventions Summary 04 November, 2004 The following LDC conventions were used in the Iraqi Arabic Appen database. Background Noise intermittent noise continuous noise -noise -end noise Speaker Noise lipsmack breath cough lau...
CALLHOME_Mandarin_Chinese_Transcripts_XML_version.doc
Path: UPenn >> LCD >> 2008 Fall, 2009
Description: CALLHOME Mandarin Chinese Transcripts XML version The XML edition of the CallHome Mandarin Chinese Transcripts corpus contains the same 120 transcripts of telephone conversions in the LDCs original release (LDC96T16). The current version is marked u...
0readme.txt
Path: UPenn >> LDC >> 2003 Fall, 2008
Description: README File for the GIGAWORD ARABIC TEXT CORPUS = INTRODUCTION - The Gigaword Arabic Corpus is a comprehensive archive of newswire text data that has been acquired from Arabic news sources by the Linguistic Data Consortium (LDC), at the Uni...
SRO_SPEC.TXT
Path: UPenn >> LDC >> 93 Fall, 2009
Description: File sro-specs.doc. originally drawn from a memo by C. Hemphill of TI (4/18/90). amended 07/91. revised by L. Shriberg (11/10/91). revised by Patti Price (12/09/91), revised 01/21/92. minor revisions made by J. Garofolo on 02/21/92. Please note: in ...
sp_lex.txt
Path: UPenn >> LDC >> 96 Fall, 2009
Description: Garrett, Susan, T. Morton, and C. McLemore. 1997. LDC Spanish Lexicon. Philadelphia: Linguistic Data Consortium, University of Pennsylvania. - Description of the LDC Spanish lexicon - CONTENTS 1. Sum...
PAPER.TXT
Path: UPenn >> LDC >> 96 Fall, 2009
Description: THE CTIMIT CELLULAR BANDWIDTH SPEECH CORPUS E. Bryan George(*), Kathy L. Brown Signal Processing Center of Technology Lockheed-Martin Sanders, Inc. Nashua, NH 03061 (*) E. Bryan George is now with the DSP Research and Development Center, Texas Instru...
