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Ep # dEp lprob lNiso dlNiso 74.081 0.058 -1.45e-05 7.348 0.182 74.143 0.066 1.09e-03 7.348 0.181 74.214 0.076 2.30e-03 7.348 0.181 74.296 0.087 3.63e-03 7.348 0.181 74.389 0.100 5.03e-03 7.348 0.181 74.496 0.114 6.51e-03 7.348 0.181 74.618 0.131 8.06e-03 7.348 0.181 74.759 0.150 9.66e-03 7.348 0.181 74.920 0.172 1.11e-02 7.348 0.181 75.105 0.198 1.24e-02 7.348 0.181 75.317 0.227 1.34e-02 7.348 0.181 75.560 0.261...
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Ep # dEp lprob lNiso dlNiso
74.081 0.058 -1.45e-05 7.348 0.182
74.143 0.066 1.09e-03 7.348 0.181
74.214 0.076 2.30e-03 7.348 0.181
74.296 0.087 3.63e-03 7.348 0.181
74.389 0.100 5.03e-03 7.348 0.181
74.496 0.114 6.51e-03 7.348 0.181
74.618 0.131 8.06e-03 7.348 0.181
74.759 0.150 9.66e-03 7.348 0.181
74.920 0.172 1.11e-02 7.348 0.181
75.105 0.198 1.24e-02...
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Berkeley - ASTRO - 00303105
# Ep lNiso51.170 7.29152.755 7.12556.026 7.56356.889 7.50757.348 7.31358.104 7.11158.442 7.64058.741 7.75459.199 7.22859.217 7.14559.540 7.35259.932 7.25860.057 7.55560.316 7.41660.567 7.28560.671 7.08061.121 7.37561.193 7.54261.36
Berkeley - ASTRO - 00303105
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Oregon State - CS - 532
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Auburn - COMP - 6620
AnthropomorphismAchilles J. Hamilothoris Comp 6620 11 October 20071Anthropomorphism Objectives Define Anthropomorphism Give some examples Pros/cons2Anthropomorphism The term is derived from two Greek words, (anthrpos), meaning human, a
Oregon State - CS - 461
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Auburn - COMP - 6620
Persuasive TechnologiesMichael FullerPersuasion Technologies being used to draw people's attention to certain kinds of information in an attempt to change what they do or think. Stop Smoking, Buy this, Do ThatThe Past In the past persuasion w
Auburn - COMP - 6620
Chapter 7IdentifyingNeedsandEstablishing RequirementsMarieKraska XiaomingLi EricaMoore MichaelSalyer1Overview Theimportanceofrequirements Differenttypesofrequirements Datagatheringtechniques Taskdescriptions: Scenarios UseCases Essent
Oregon State - ENGR - 430
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Oregon State - ENGR - 430
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Oregon State - ENGR - 430
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Oregon State - ENGR - 430
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Oregon State - ENGR - 430
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Oregon State - ENGR - 430
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Oregon State - ENGR - 430
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Oregon State - ENGR - 430
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Oregon State - ENGR - 430
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Oregon State - ENGR - 430
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Oregon State - ENGR - 430
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Oregon State - ENGR - 430
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Oregon State - ENGR - 430
# # 9#ch19-belief-nets.ps# # ( Oq6 #{v7a#&1{cC 5t %z7 $ #3 *{lO#UI#A #A wK# #}?~? #_^#w}:t7#y#o# #_#_|F97x _lf_| {# #_}5o?<>| ~av ^ xpN\w/~ R? taFNo_ _#zI\#o# c_s2=| #_e#nn/ W>~#^~}# ow O /y#_kwx<iA|uZ~? ~#_ _t{}#tF#o>#>:t_#. /g#d o~x>_#o;|o
Oregon State - CS - 472
Chapter 4: Arithmetic Where we've been: Performance (seconds, cycles, instructions) Abstractions: Instruction Set Architecture Assembly Language and Machine Language What's up ahead: Implementing the Architectureoperationa32ALUresult32
Oregon State - CS - 472
Chapter 5: The Processor: Datapath & Control We're ready to look at an implementation of the MIPS Simplified to contain only: memory-reference instructions: lw, sw arithmetic-logical instructions: add, sub, and, or, slt control flow instructions
Arkansas State - CIT - 6483
ERP by Mary SumnerChapter 1 Table 1 -1 Historical Evolution of ERP SystemsIndependent vs. Dependent DemandIndependent Demand (Demand for the final endproduct or demand not related to other items) use forecasting)Finished productE(1 )Compon
Arkansas State - CIT - 6483
HOW GREAT PLAINS BE A T S MAS 90/200COMPARATIVE ANALYSIS & REVIEWBy J. Carlton Collins, ASA ResearchMarch, 2005WWW.ASARESEARCH.COMCOMPETITIVE ANALYSIS AND REVIEWHOW GREAT PLAINS BEATS MAS 90/200 TABLE OF CONTENTSIntroduction & Scope of W
Arkansas State - CIT - 6483
Time Year Qtr Period 2002 1 1 2 2 3 3 4 4 2003 1 5 2 6 3 7 4 8 2004 1 9 2 10 3 11 4 12 2005 1 13 2 14 3 15 4 16 2006 1 17 2 18 3 19 4 20 alpha beta gammaActual Sales $684.2 $584.1 $765.4 $892.3 $885.4 $677.0 $1,006.6 $1,122.1 $1,163.4 $993.2 $1,312
Oregon State - ENGR - 430
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MD University College - COMM - 393
Grading Criteria for a Scholarly AbstractName: Points: Grade:A (90-100) Performance exceeds expectations. B (80-89) Performance successfully meets expectations. C (70-79) Performance does not meet expectations. F (69- ) Performance falls serious
MD University College - COMM - 393
Example of a Graphic for Use in Small Assignment: Integrating a Graphic into the Text of Your Report Here is a bar graph of the perceived audience for the Facebook profiles of 800 Michigan State University students. The source of the graph is Ellison
MD University College - COMM - 600
First Words of Title 1Running Head: ABBREVIATED TITLETitle of Your Paper Your Name University of Maryland University Collegesychological Associat ion. (2001). Publ i cati on manual of the Amer i can Psychol ogi cal Associ ati on (5t h ed.). Cit
MD University College - COMM - 600
First Words of Title 1Title of Your Paper Your Name University of Maryland University Collegean Psychological Association. (2001). Publication manual of the American Psychological Association (5th ed.). City placeWasument. 010009000003780000000
MD University College - COMM - 600
Using Tables and Figures in an Academic Research PaperCOMM 600 Dr. Nancy HoaglandPurpose of this LectureBasic information about the effective use of tables and figures. See the APAPublication Manualfor detailed guidelines on creating tabl
MD University College - COMM - 600
Mini-Lecture: Using Feedback to Improve Your WritingBy now should have received feedback on your academic research paper from your teacher and from a colleague. You will have an opportunity to revise your paper to receive a better grade and also to
MD University College - COMM - 600
Name: Rubric for Scholarly AbstractGrade:Write a scholarly abstract (150-200 words) of a journal article in your field of study. Grading Scale: F (-69); C (70-79); B (80-89); A (90-100) Low Performance At or Below Average At or Above Average Exemp
MD University College - COMM - 600
Name: Rubric for Weekly Online ConferencesGrading Scale: F (0-4 ); C (5); B (6); A (7)Grade:(Note: 14 weeks x 7 points = 98 points; Introduction = 2 points for a total of 100 points for the Course Participation Grade) Low Performance At or Below
Georgia Tech - CS - 6660
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Georgia Tech - CS - 6660
Intelligent Agents MotivationHuman Being Nilsson - Chapter 1 (Russell and Norvig - Chapters 1 and 2)senseactNOTE different people have different opinions about what artificial intelligence is all about these slides contain my opinionsEnviron
Georgia Tech - CS - 6660
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Georgia Tech - CS - 6660
agentsArtificial Intelligence Uninformed SearchNilsson - Chapters 7 and 8 (Russell and Norvig - Chapter 3) sense Software System = AgentactEnvironmentPerformance MeasureUninformed Search; page 1 of 26Uninformed Search; page 2 of 26perc
Georgia Tech - CS - 6660
Artificial Intelligence Game PlayingNilsson - Chapter 12 (Russell and Norvig - Chapter 5)May 1997 Deep Blue - Garry Kasparov 3.5 - 2.5machines are better than humans in: othello machines and humans are about equally good in: checkers (draughts),
Georgia Tech - CS - 6660
credit card application (1)Artificial Intelligence Decision TreesNilsson - briefly mentioned in Chapter 3 (Russell and Norvig - Chapter 18) information from your application: how long have you lived at your current address? what is your salary? do
Georgia Tech - CS - 6660
inductive learning problemArtificial Intelligence (Artificial) Neural NetworksNilsson - Chapter 3 (Russell and Norvig - Chapter 19) pronunciation (cat vs. cent) handwritten character recognition face detection driving "neural networks are the seco
Georgia Tech - CS - 6660
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Georgia Tech - CS - 6660
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Georgia Tech - CS - 6660
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SUNY Albany - STA - 552
HYPOTHESIS TESTING (TWO SAMPLE) - CHAPTER 81PREVIOUSLY estimation how can a sample be used to estimate the unknown parameters of a population use confidence intervals around point estimates of central tendency (mean) and variability (variance, s
SUNY Albany - STA - 552
PROBABILITY - CHAPTER 41UP TO NOW. Chapter 2: Types of Data (NOIR, Discrete/Continuous), Descriptive Statistics (Central Tendency, Variation), Graphical Display (Histogram, Stemand-Leaf, Box Plot) Probability (Classical, Relative Frequency, Subj
SUNY Albany - STA - 552
Suggested extra problems for Chapter 4.Binomial and Poisson Distributions.in handout from class on 2/27.page 168: 9, 11, 13page 174-177: 1-8, 12, 13, 15, 16-20, 21-25, 27, 29-30page 180-181: 1-6, 8-11, 13page 184-185: 1-4, 6, 8-10PAGE 189
SUNY Albany - STA - 552
Nonparametric Methods Overview All of what we have done thus far assumed that we were working with data that came from some underlying distribution. Most of the time we assumed that the data were normally distributed (or that the Central Limit Theore
SUNY Albany - STA - 552
Probability and Probability Distributions - Revised 2/26 Overview The Descriptive Statistics module showed how to describe data by computing certain measures of location and certain measures of variation. You also learned how to construct certain gra
SUNY Albany - STA - 552
Problem NumbersThe following shows problem numbers that correspond in V6 and V5 of Rosner. It will be updated as the semester progresses.Descriptive Statistics V6: 2.1 - 2.3 V5: 2.1 - 2.3 V6: 2.12 - 2.18 V5: 2.11 - 2.17 V6: 2.31 - 2.32 V5: 2.29 -
SUNY Albany - STA - 552
DESCRIPTIVE STATISTICS - Chapter 2NOTE: Some of these problems have EXACT answers (e.g. what's the MEAN), while others ask for your impression (what do the statistics or graphics indicate). There's a bit of leeway in the answers that require impress
SUNY Albany - STA - 552
Probability - Chapter 3these problems can be solved with formulas you can find in chapter 3 or by using other concepts such as 2 x 2 tables and sample spaces13.12Are events independent, given. F:FATHER P(M )= 0.1 P(F) = 0.1 P(M1F) = 0.02M:MO
SUNY Albany - STA - 552
Probability Problems - Chapter 3 Answers13.14What is the probability of side effects from at least one agent?a represents side effects from agent A b represents side effects from agent B You are told that the events are independent, so. P(a 1
SUNY Albany - STA - 552
Probability - Chapter 4 (EXTRA PROBLEMS)4.53 probability of otitis media in 6 months? in one year?given the data in table 4.16 . out of 2500 disease free infants at the start of the year, 1875 are disease free after 6 months . so 2500 - 1875 = 625
SUNY Albany - STA - 552
Probability - Chapter 4 NOTE: Pc=complement of P4.1 A "probability-mass function" for X is Rosner's terminology for a probability distribution. there can be 0, 1, or 2 hypertensive adults. A = mother's DBP >= 95 B = father's DBP >= 95 P(A) = .1 P(B
SUNY Albany - STA - 552
Probability - Chapter 5 - Extra Problemscensus data say that the prevalence of cervical cancer is 0.2% 5.16 new data show that out of 100,000 women, 100 have cervical cancer . is the new data consistent with the census data? . use the normal approx
SUNY Albany - STA - 552
Probability - Chapter 55.6 What percentage of boys in this age range have carbohydrate intake above 140 g / 1000 cal ? You are told that you can assume that carbohydrate intake us normally distributed and that the mean intake is 124, with a standar
SUNY Albany - STA - 552
Estimation - Chapter 66.7 6.8standard deviation of LVEF from table 6.9 = 0.080 standard error of the mean of LVEF from table 6.9 = 0.015 if you had entered the data in Statcrunch, your answers would look as follows .both the standard deviation an
SUNY Albany - STA - 552
Estimation - Chapter 66.7 standard deviation of LVEF from table 6.9 = 0.080 6.8 standard error of the mean of LVEF from table 6.9 = 0.015 6.10 2.583 (from table 5, page 831 in Rosner) 6.11 -1.313 (from table 5, page 831 in Rosner) 6.12 2.365 (from
SUNY Albany - STA - 552
Problem Answers Chapter 77.1 Ho: = 1.0 H1: . 1.0 This is a one-sample z-test (you are given the population standard deviation). The data are continuous, values of serum-creatinine.using equation 7.13. z = (1.2 - 1.0) / (0.4 / r12) = 1.73 The crit
SUNY Albany - STA - 552
Problem Answers Chapter 88.1 Ho: = 0 H1: . 0 This is a paired t-test with a null hypothesis that the mean change = 0 versus the alternative that the mean change does not = 0 (two sided alternative). Using equation 8.4 on page 299 of Rosner with t