Ppt on central limit theorem for dummies

Econ. 4132 Senior Economic Seminar Ken Taylor Spring 2010  Prerequisites: 1. Econ. 2101 & 2102 2. Completion of the Statistics course requirement. 3.

firms to the market, total transactions costs for the economy are minimized. d. The limit of the firm varies over time: technological /the problem and also draws upon known mathematical theorems to aid in reasoning. The major difference between/reported “critical value”). Dummy Variable Technique… - Multiple regression can be used to analyze the effects of qualitative variables. - Dummy variable = a / and invention; but destroys as well. 12.Centralized government…boosted in post-WWII Germany and Japan /


Probability Tables. Normal distribution table Standard normal table Unit normal table It gives values.

that supplies the samples doesn’t have to be a normal distribution for the Central Limit Theorem to hold. What if the population is a normal distribution? In that case, the sampling distribution of the mean is a normal distribution regardless of the sample size. J. Schmuller, Statistical Analysis with Excel For Dummies The limits of confidence Sampling distributions help you to answer the question: How much/


Analysis of Cross Section and Panel Data Yan Zhang School of Economics, Fudan University CCER, Fudan University.

) is not constant, the usual t statistics and confidence intervals are invalid no matter how large the sample size is; the central limit theorem does not bail us out when it comes to heteroskedasticity. is a consistent estimator of 4.2 TESTING HYPOTHESES: A Single Population/ (and the same error term u). Intercept Shift  Dummy Variable Trap: to keep track of which group is the base (benchmark) group. we will always include an overall intercept for the base group. Nothing changes about the mechanics of OLS/


BA 333 Operations Management

is Represented by Node as the Completion of an Activity Arrows Represent the Sequential Linkages Between Activities For Example, Node 1 is Begin, Node 2 is Complete Task 1, Node 3 is Complete /3 Correct 1-2 2-4 4-5 1 2 4 5 2-3 3 3-4: Dummy activity Network Diagramming First Step in Project Management Begins with a Work Breakdown Lists the “WHAT’/ Estimate = Sigmae te = (a + 4m + b)/6 Sigmae = (b - a)/6 Can Use Central Limit Theorem to Estimate Project Time Example Network Flow Diagram 7 6 5 4 3 2 1 A G C I H /


Review of material from previous week: Variance

distribution would start to look like the theoretical probability distribution. For an infinite number of experiments, the frequency and probability distributions would be identical. Significance of Sample Size Central Limit Theorem: the larger the sample size, the greater the probability that/others would get a zero on the variable. Let’s create a dummy variable for the variable “Country of origin” in the Cars.sav data set. The new dummy variable will be “American in Origin.” If you look at the country/


MASSIMO FRANCESCHETTI University of California at Berkeley Phase transitions an engineering perspective.

Theorem Consider annuli shapes A(r) of inner radius r, unit area, and critical density For all, there exists a finite, such that A(r*) percolates, for all It is possible to decrease the percolation threshold by taking a sufficiently large shift ! CNP Squishing and squashing Shifting and squeezing What have we proven? CNP Among all convex shapes the hardest to percolate is centrally/dummy” observation when then take the limit for  t =0 Derive Kalman equations using a dummy observation Then take the limit for/


CLIC-ILC Cost & Schedule Working Group Meeting TILC09 – Sunday, April 19, 2009 reported by Peter H. Garbincius CLIC_ILC_phg_21april09.ppt.

for probabilistic cost analysis Identify sources of cost variance and separate deterministic effects Identify correlated random effects and estimate their standard deviations (not to be added quadratically!) Estimate mean value and standard deviation of independant elementary costs and modelize by simple skew law, e.g. exponential Apply central-limit theorem/ test … dummy data sets links CLIC-ILC Cost & Schedule 16 http://www-ilcdcb.fnal.gov/example_26march09-Construction.xls Use EDMS for archive, approval/


Bootstrap Event Study Tests Peter Westfall ISQS Dept. Joint work with Scott Hein, Finance.

in the variance formula; Estimation of the variance gives another minor correction: T n-1 instead of Z critical and p-values) The central limit theorem does not apply since we are concerned with the distribution of Y 0, not the distribution of The Distribution of (Y 0 -  /statistic is Z =  t i /(g 1/2 s t ), where t i is the t-statistic from the univariate dummy-variable-based regression model for firm i, and s t is the sample standard deviation of the g t-statistics. Algorithm: (i) create a pseudo-/


Operations Research II Course,, September 20131 Operations Research II Part 1: Project Management Dr. Aref Rashad Part 2: Network Flow Part 3: Inventory.

1 2 3 a b Operations Research II Course,, September 201329 EXAMPLES OF THE USE OF DUMMYACTIVITY Dummy RIGHT 1 1 2 Activity c not required for e a b c d e a b c d e WRONG !!! RIGHT Network concurrent activities/activities. The project variance is the sum of the variances of the critical path activities. The expected project time is assumed to be normally distributed (based on central limit theorem). In example, expected project time (t p ) and variance (v p ) interpreted as the mean (  ) and variance (  2 )/


Page 1 Lecture 17 The applications of tomography: LTAO, MCAO, MOAO, GLAO Claire Max AY 289 UC Santa Cruz March 7, 2013.

k y,k z volume kXkX kZkZ Fourier slice theorem in tomography (Kak, Computer Aided Tomography, 1988) / Tip-Tilt at altitude → Dynamic Plate Scale changes Credit: Rigaut, MCAO for Dummies Page 17 Outline of lecture Review of AO tomography concepts AO applications of /modest correction over a very wide field of view Central parts of the globular cluster Omega Centauri, as/ can take advantage of smaller image –Potential improved SNR for background-limited point sources Page 56 Credit: A. Tokovinin time Page/


Urban and Regional Economics Weeks 8 and 9 Evaluating Predictions of Standard Urban Location Model and Empirical Evidence.

measures Income time dummies, other locational dummies Examine findings Updated /central core less attractive as costs of land increase lowers Office Bid Rent Reduces employment density R u R Office R residential R ag. Service limit Your book looks at other examples of these effects You are responsible for/limited controls. Consequences More multifamily housing. Smaller lot sizes in some areas. Industrial and commercial activities separated. More strip malls. Neighborhood covenants used  Coase Theorem/


Class 2 Statistical Inference Lionel Nesta Observatoire Français des Conjonctures Economiques CERAM February-March-April.

us that the sample mean will converge to the population (true) mean as the sample size increases.  Central Limit TheoremCentral Limit Theorem tells us that for many samples of like and sufficiently large size, the histogram of these sample means will appear to be /true?  Produce descriptive statistics labour comparing the two groups  Produce a group variables which equals 1 for US firms, 0 otherwise  This is called a dummy variable  Write out H 0 and H 1  Analyse  Comparer les moyennes  Test t /


Ka-fu Wong © 2007 ECON1003: Analysis of Economic Data Lesson11-1 Lesson 11: Regressions Part II.

random variable is distributed as Student ’ s t with (n – k - 1) degrees of freedom. In addition the central limit theorem enables us to conclude that this result is approximately valid for a wide range of non-normal distributions and large sample sizes, n. t= (b j –  j ) / / )/Y 1 ) ≈ (Y 2 -Y 1 )/Y 1 Ka-fu Wong © 2007 ECON1003: Analysis of Economic Data Lesson11-78 Dummy Variables A dummy variable is a categorical independent variable with two levels: yes or no, on or off, male or female recorded as 0 or 1/


Lecture Outline Methodological Challenges Examples Recent Publications My Cleveland Application.

a 46 percent error in one simulation! Average MWTP is a limited concept, even with the correct specification; it cannot be compared across/ parametric forms for a hedonic as special cases. It allows for household heterogeneity. It leads to tests of key sorting theorems. My Envelope/ 0.1414***0.0067 Air Cond.House has central air conditioning 0.0254***0.0055 FireplacesNumber of fireplaces/2(Minority Teachers 1) squared- 0.7161*0.3921 Cleveland SD Dummy for Cleveland & E. Cleveland Schl. Dists. 0.47550.3269 Near/


Chapter 8 Network Models. 2 Four Specific Network Models Shortest Path Problem( 最短路徑問題 ) Maximum Flow Problem ( 最大流量問題 ) CPM( 要徑法 ) and PERT ( 計畫評核術 )

max LT(i) min 100 299 399 416 526 638 Example 6: 1 65 4 2 3 A 6 B 9 Dummy C 8 D 7 E 10 F 12 54 For an arbitrary arc representing activity (i,j), the Fotal Float (TF), represented by TF(i,j), of the activity/on a critical path found by CPM. PERT assumes that the critical path found by CPM contains enough activities to allow us to invoke the Central Limit Theorem and conclude that the following is normally distributed: : expected duration of activities on any path : variance of duration of activities on any /


Lecture Outline Methodological Challenges Examples Recent Publications My Cleveland Application.

not appropriate because it rules out sorting. Average MWTP is a limited concept, even with the correct specification; it cannot be compared/parametric forms for a hedonic as special cases. It allows for household heterogeneity. It leads to tests of key sorting theorems. Estimates / 0.1414***0.0067 Air Cond.House has central air conditioning 0.0254***0.0055 FireplacesNumber of fireplaces/2(Minority Teachers 1) squared- 0.7161*0.3921 Cleveland SD Dummy for Cleveland & E. Cleveland Schl. Dists. 0.47550.3269 Near/


Externalities.

measurement of political representativeness DROUGHT-captures environmental effects PROPERTY, COMMAID-dummy for 2 types of property rights regimes over elephants in Africa /central planner—what if we relax this assumption? Pigovian taxes can “break” Coase Theorem Factory imposes costs on houses of $60 K per year. Govt imposes Pigovian taxes of $60K Factory can install soundproofing for/“captured” Quotas Quotas are a method of setting allowable limits on output or the usage of externality creating inputs. /


Information Security Lab., CSIE, NCYU, Taiwan, R.O.C.1 Some Issues in Network Security Authentication, Fair Exchange and Intrusion Detection Department.

replays the same question one time. Corollary 1: Similar to theorem 2, an intruder can found the window W in expected trials/ NCYU, Taiwan, R.O.C.38 Confirmer Signatures with Limited Verifiers Malicious confirmer The confirmer may prove the correctness of the/ NCYU, Taiwan, R.O.C.55 Honeypot Honeypot uses the dummy or virtual environment (i.e. a true system of low security/ The centralization recording work, in RMS host, will not influence the general host system on efficiency and is good for system managers/


Class 2 Statistical Inference Lionel Nesta Observatoire Français des Conjonctures Economiques SKEMA Ph.D programme 2010-2011.

regardless of the form of the underlying distribution of the population, provided that the sample size is large enough.  Central Limit Theorem tells us that for many samples of like and sufficiently large size, the histogram of these sample means will appear to be a normal / true?  Produce descriptive statistics labour comparing the two groups  Produce a group variables which equals 1 for US firms, 0 otherwise  This is called a dummy variable  Write out H 0 and H 1  Run the student t test  What do you/


6.852: Distributed Algorithms Spring, 2008 Class 20.

Irreducibility Theorem [Chandra, Hadzilacos, Jayanti, Toueg] Theorem: For /on each port is limited to a designated “/ to the centralized simulation. –/For every port i, one i-perform task, one i-output task. Explicitly program fault-tolerance: –Keep track of which ports have failed. –When > f failures have occurred, the object need not respond to anyone (but it might). –When  f failures have occurred, the object must respond to every invocation on a non-failing port. –Convention: Each i-task includes a dummy/


Digital Image Processing, 2nd ed. www. imageprocessingbook.com © 2001 R. C. Gonzalez & R. E. Woods 1 Objective To provide background material in support.

and The second expression may be written as which is known as Bayes theorem, so named after the 18th century mathematician Thomas Bayes. Digital Image Processing,/For example, the second, third, and fourth central moments are intimately related to the shape of the probability density function of a random variable. The second central moment (the centralized/the integral operator between the limits  and x. Then, the output in terms of the input is given by where w is a dummy variable of integration. This /


Project and Production Management

b a c e 1 2 5 6 d 4 DUMMIES FOR UNIQUENESS OF ACTIVITY REPRESENTATION EXAMPLE 5 S T DUMMIES FOR CREATION OF A SINGLE SOURCE AND SINK THE ROLE OF DUMMIES IN PROJECT NETWORKS Role of Dummy I II III Network type A-O-A yes yes/STANDARD PERT ASSUMPTIONS 1. The activities are independent 2 The critical path contains a large no. of activities so that we can invoke the Central Limit Theorem. 3 .All activities not on the critical path are ignored. 4. Activity times follow a Beta distribution. 5. The mean and /


TECHNIQUES FOR PLANNING & MANAGING PROJECTS.

PERT DIAGRAMMING (DUMMY ACTIVITIES) Uses 1. Clarify Precedence A C A B Dummy B OK D C D OK, but different meaning PERT DIAGRAMMING (DUMMY ACTIVITIES) Uses 2. Clarify Event Numbers A A C B B Dummy Not correct, OK in concept C PERT DIAGRAMMING (DUMMY ACTIVITIES) Uses /10 9 5 G 11 J B H D 8 61 3 E 7 Network Paths for the St. Adolf’s Hospital Project PROBABILITY OF MEETING THE PROJECT DUE DATE The central limit theorem allows us to use the normal probability distribution to find the probability of achieving a /


Formalizing the Concepts: Simple Random Sampling.

is widely used. Part of its appeal is that it is well behaved and mathematically tractable. Central limit theorem Sample variance and standard error  Variance of the sample mean of an SRS of ‘n’ units for a population of size ‘N’:  e = standard error  Measure of sampling error./Unknown, but can be estimated without bias by: Proportions  A proportion P (or prevalence) is equal to the mean of a dummy variable.  In this case Var(P) = P(1-P), and  It is not sufficient to simple report the sample proportion/


ECE Department Florida Institute of Technology ECE 5221 Personal Communication Systems Introduction to GSM.

synchronization, dummy, access  Format of a burst defied by its function  DL: normal, frequency correction, synchronization, dummy  UL: normal, access Time/Frequency/Amplitude diagram for GSM normal/central frequency of the carrier  Only on the forward link Spectral characteristics of the control channel. The peak in the spectrum allows for/ Page 37 Sampling and Quantization  Sampling oSampling theorem specifies conditions for discretization of band limited analog signals oVoice needs to be sampled at /


1. Descriptive Tools, Regression, Panel Data. Model Building in Econometrics Parameterizing the model Nonparametric analysis Semiparametric analysis Parametric.

, known generalities about properties: Use bootstrapping Root N consistency Sampling conditions amenable to central limit theorems Compute by resampling mechanism within the sample. Bootstrapping Method: 1. Estimate parameters using/unobservables) Approaches (Parametric) Control Function: Build a structural model for the two variables (Heckman) (Semiparametric) Instrumental Variable: Create an instrumental variable for the dummy variable (Barnow/Cain/ Goldberger, Angrist, current generation of researchers)/


TRIBE statistics course Split, spring break 2016 Introduction.

way Central limit theorem: the magic of normally distributed sample means 14 Module 3 Correlation: causality from content, not statistics Linear regression: standard ordinary least squares (OLS) Error term: model change and transformations for ideal characteristics/of missing data, no need in statistical software If at all, then for all variables equally (for cross-variable relations) Expansion of the data set (additional variables, often dummies) Beware of implicit assumptions (A + B = Total: maybe there/


Computational Social Science: Theories, Methods and Data 3/5 Ingmar Weber Qatar Computing Research Institute Please interrupt me at any.

2012. Can cope with less data by making certain modeling assumptions. 77 Regression Discontinuity (RD) Future performance Coarsend test score Dummy Var: received schoolarship or not 78 RD Robustness Checks –Can individuals control if they are above or below the threshold? –/higher? Av(T) = 45.25 vs. Av(C) = 38.00 Is this significant? t-test assumes that Av(*) is normally distributed Central Limit Theorem holds for “large n” Small n: Fisher’s Exact Test Observed: Av(T) – Av(C) = 7.25 Permutation 1: Av(T) – /


Lecture 3 Ordinary Least Squares Assumptions, Confidence Intervals, and Statistical Significance.

values follow, the sample mean will follow a Normal distribution if the sample size is large.” Central Limit Theorem: Sample Size How large must n be for the CLT to hold? depends on how far the population distribution is from Normal the further from/ from a normal distribution No explanatory variable is an exact linear function of other explanatory variables (important with dummy variables) Interpretation of the Regression Coefficients The value of the dependent variable will change by j units with/


Econometrics - Lecture 1 Introduction to Linear Regression.

central tendency, measures of dispersion, measures of association, histogram, frequency tables, scatter plot, quantile Theory of probability: probability and its properties, random variables and distribution functions in one and in several dimensions, moments, convergence of random variables, limit theorems/ 3294 individuals (1569 females) Average wage p.h.: 6,31$ for males, 5,15$ for females Model: wage i = β 1 + β 2 male i + ε i male I : male dummy, has value 1 if individual is male, otherwise value 0 OLS /


If you are viewing this slideshow within a browser window, select File/Save as… from the toolbar and save the slideshow to your computer, then open it.

but we could instead use AgeGroup, with several levels; e.g., child, adult, elderly. Stats packages turn each level into a dummy variable with values of 0 and 1, then treat each as a numeric variable. Example: Strength = a + b*AgeGroup is /to have values that come from a normal (bell-shaped) distribution. This assumption can be violated. Testing for normality is silly. The Central Limit Theorem assures a normal sampling distribution. With a count as the dependent, the error has a Poisson distribution, which/


Lecture Outline Methodological Challenges Examples Recent Publications My Cleveland Application.

for some unobservable factors, but may also introduce problems. Problems with Fixed Effects They all limit the variation in the data for estimating capitalization. School district fixed effects, for/for a hedonic as special cases. It allows for household heterogeneity. It leads to tests of key sorting theorems/ Air Cond.House has central air conditioning 0.0254***0/ Cleveland SD Dummy for Cleveland & E. Cleveland School Districts 0.14140.2232 Table 5A. Specification Tests and Results for Key School Variables/


Svet Brainov University at Buffalo 210 Bell Hall Buffalo, NY 14260 SAC 2002 Tutorial Henry Hexmoor Henry Hexmoor University of Arkansas Engineering Hall,

robust, elegance n Disadvantages u Modeling limitations, correctness, realism Multiagents: Formal and/ they have duration.  Situation Shortcomings: central decision making Multiagents: Formal and Economic 3/acting Laws, Norms, Conventions, Commitments Motivations for team formation: Shortcomings in ability Efficiency Failure/&Braynov Example (cont.) n Revenue equivalence theorem [William Vickrey, 1961]: The first- / discern the original function. n Using dummy items and functions. n Watermarks and /


5/18/2015 L. K. Gaafar PROJECT MANAGEMENT Time Management* Dr. L. K. Gaafar The American University in Cairo * This Presentation is Based on information.

05B 714 7 07D 79 1315 62C 1416 14 16 02F 910 1516 61E Dummy Time Management ESEF LSLF SlackDur. Act Key Activity-on-node network 5/18/ K. Gaafar Important Distributions 5/18/2015 L. K. Gaafar Stochastic Times The Central Limit Theorem The sum of n mutually independent random variables is well-approximated by a normal distribution/DC 5712 EATriangular336 FA, BTriangular588 GE, DUniform9NA9 Construct an activity-on-arrow network for the project above. Provide a 95% confidence interval on the completion time of /


Copyright © 2004 Brooks/Cole, a division of Thomson Learning, Inc. 1 Chapter 8 Network Models to accompany Operations Research: Applications & Algorithms,

path found by CPM contains enough activities to allow us to invoke the Central Limit Theorem and conclude that the following is normally distributed: Copyright © 2004 Brooks/Cole, a division of Thomson Learning, Inc. 42 a, b and m for activities in Widgetco Activityabm (1,2)5139 (1,3)2106 (3,5/ slide: Copyright © 2004 Brooks/Cole, a division of Thomson Learning, Inc. 44 Of course, the fact that arc (2,3) is a dummy arc yields E(T 23 )=varT 23 =0 The critical path was 1-2-3-4-5-6. Thus, E(CP)=9+0+7+10/


Statistical Inference and Regression Analysis: GB.3302.30 Professor William Greene Stern School of Business IOMS Department Department of Economics.

k () may be any function of data. Examples: Logs and levels in economics Time trends, and time trends in loglinear models – rates of growth Dummy variables Quadratics, power functions, log-quadratic, trig functions, interactions and so on. 78/97 Linearity Simple linear model, E[y|x] =x’β / Observations are independent Assumption will be unnecessary – we will use the central limit theorem for the statistical results we need. 89/97 The Linear Model y = X  +ε, N observations, K columns in X, /


CEM 510 Construction Planning and Scheduling Detailed Course Outline Dr Soliman Almohawis.

 Dependency  Continuity Outline Contd..  Dummy activities  Numbering  Efficiency Procedures for drawing the network  Activity list  Dependency  Redundancy  Approaches  Homework Assignment 3 Rules for Diagramming  Representation of activity.  Representation /PERT) Outline: CPM vs PERT Advantages of PERT Statistics Review Central Tendency Dispersion Probability Distribution Function Central Limit Theorem Outline Cont: PERT Assumptions Activity Assumption Network Assumptions Example Homework /


Copyright © 2003 Brooks/Cole, a division of Thomson Learning, Inc. 1 Chapter 8 Network Models to accompany Introduction to Mathematical Programming: Operations.

path found by CPM contains enough activities to allow us to invoke the Central Limit Theorem and conclude that the following is normally distributed: Copyright © 2003 Brooks/Cole, a division of Thomson Learning, Inc. 46 a, b and m for activities in Widgetco Activityabm (1,2)5139 (1,3)2106 (3,5/ slide: Copyright © 2003 Brooks/Cole, a division of Thomson Learning, Inc. 48 Of course, the fact that arc (2,3) is a dummy arc yields E(T 23 )=varT 23 =0 The critical path was 1-2-3-4-5-6. Thus, E(CP)=9+0+7+10/


CEM 510 Construction Planning & Scheduling Dr Almohawis

Topic 3 Outline Contd.. Dummy activities Numbering Efficiency Procedures for drawing the network Activity list Dependency Redundancy Approaches Example Homework Assignment # 3 CEM-510- Topic 3 Topic 4: Precedence Diagramming Outline: Rules for Diagramming Representation of activity. / Outline: CPM vs. PERT Statistics Review: Central Tendency and Dispersion Probability Distribution Functions Central Limit Theorem PERT Assumptions Activity Assumptions Network Assumptions PERT Computations Examples.


SKMA 3812 – Aviation Management SKMA 3812 - Aviation Management Project management - Network Analysis Project Management SKMA 3812 - Flight Management.

the same time (concurrently). A dummy activity shows a precedence relationship but reflects no passage of time. Two or more activities cannot share the same start and end nodes. Expanded Network for Building a House Showing Concurrent Activities/ is the sum of the variances of the critical path activities. The expected project time is assumed to be normally distributed (based on central limit theorem). In example, expected project time (t p ) and variance (v p ) interpreted as the mean (  ) and variance /


8-1 Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall Project Management Chapter 8.

Network Concurrent Activities Figure 8. 6 Concurrent activities for house-building project 8-21 Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall ■A dummy activity shows a precedence relationship but reflects no passage/ variance is the sum of the critical path activities’ variances ■The expected project time is assumed to be normally distributed (based on central limit theorem). ■In example, expected project time (t p ) and variance (v p ) interpreted as the mean (  ) and variance/


Solid State Physics Lecture 15 HW 8 Due March 29 Kittel Chapter 7: 3,4,6 The free electron standing wave and the traveling wave are not eigenstates. To.

IF: Then Will have the form required so that there is a meaningful translation operator. In the limit of large N, the plane wave can have any wavelength. This is a symmetry argument… nothing much/to make the middle term a sum over k’. (It’s a dummy variable anyway.) Then the middle term has the same exponent (ikx) if k’+G = k i/ we can get a good answer with only a few terms in the central equation for the lower energy bands. Bloch Theorem Revisited. From the CE, the wavefunction is: u k (x) is /


Project Management An interrelated set of activities with definite starting and ending points, which results in a unique outcome for a specific allocation.

for house-building project The Project Network Dummy Activities A dummy /for Windows Output Probabilistic Activity Times The Southern Textile Company Earliest and latest activity times Probabilistic Activity Times Expected Project Time and Variance Expected project time is the sum of the expected times of the critical path activities. Project variance is the sum of the critical path activities’ variances The expected project time is assumed to be normally distributed (based on central limit theorem/


Student Engagement and Learning: The Pedagogical and Practical Value of Formal Work Placements Andrews, J*, Green, J.P**, Higson, H.E*, and Jones, C.M*

differentiating factor is whether a student has completed a placement or not, then providing the sample sizes are large enough for the central limit theorem to apply, ANOVA, standard OLS regression, or indeed a simple comparison of means will provide a test of whether / simply the average final year mark and the primary interest is the coefficient for the placement dummy variable. For each sub-sample of students it is clear that going on placement improves performance, the variable is positive /


Statistics in Medicine Unit 8: Overview/Teasers. Overview Regression I: Linear regression.

is most important for small samples; large samples are quite robust against this assumption because of the central limit theorem (averages are normally distributed even when the underlying trait is not!). 2. Homogeneity of variances For linear regression, the/does linear regression handle categorical predictors? Binary Treats the “0” and “1” as quantitative (numbers)! Categorical Dummy coding! Re-code the categorical predictor as a series of binary predictors! Binary variables Imagine that vitamin D was/


Micheal Walfish, Mythili Vutukuru, Hari Balakrishnan, David Karger, and Scott Shenker Presented by Corey White.

central mechanism  Protects the server from overloads and performs encouragement.  Virtual auction is Speak-Up’s main form of encouragement  The thinner makes clients automatically send a congestion-controlled stream of dummy/ bot clients  Unequal request/spoofing: charges clients for harder request when having an unequal request load. /B+G/c.  Robustness to Cheating  Theorem: “In a system with regular service intervals/ browser and it holds dummy data (1MB reflects some browsers limits on POSTs).  The/


Strategic Project Management1SPM Basic PERT/CPM (Part 2) The Concept of Float zActivities that are not on the critical path contain positive slack or float.

expected time to completion of the critical path is the sum of the mean activity times for the activities on the critical path. Strategic Project Management15SPM Basic PERT/CPM (Part 2) /,3) with variance of 2.78 y(3,4) with variance of 0.00 (dummy) y(4,5) with variance of 0.11 y(5,6) with variance of 0/ Basic PERT/CPM (Part 2) Probability Statements about Project Completion zFirst, we invoke the Central Limit Theorem y We will assume that the distribution of completion time is approximately Normal. yThis is /


Michael Walfish, Mythili Vutukuru, Hari Balakrishnan, David Karger, and Scott Shenker Presented by Sunjun Kim, Donyoung Koo 1DDoS Defense by Offense.

DDoS Defense by Offense 8 Currency-based approach – Bandwidth for Currency Central mechanism – Thinner, Server front-end Thinner – Front-end/Cause client to send more traffic Proportional Reduction – Rate limiting  Way to limit requests to server  c requests/sec – Proportional Allocation/depends on application Approach II cannot claim that good clients get Theorem – If any client transmit ε fraction of average bandwidth it / Offense 30 Mean time to upload dummy bytes for good requests DDoS Defense by Offense/


Social Network Analysis American Sociological Association San Francisco, August 2004 James Moody.

Networks: Flow Without full network data, you can’t distinguish actors with limited information potential from those more deeply embedded in a setting. a b c/if removed from the group, would disconnect the group. Equivalently (by Menger’s Theorem): (b)A group’s structural cohesion is equal to the minimum number of/ the friends,etc. Structural indicators: Such as: Centrality, dummies for group / role membership, etc. These models are the only option for ego-network data,where information on network alters is/


1 Project Planning, Scheduling and Control Project – a set of partially ordered, interrelated activities that must be completed to achieve a goal.

merge and burst event. Are these rare or what? 17 Dummy activity ACADBDACADBD W R O N G 7 5 6 9 10 A B C D /minimum time subject to the precedence constraints. In addition, CPM provides: Starting and ending times for each activity Identification of the critical activities (i.e., the ones whose delay necessarily delay /4-5)6612711 G (5-6)612181242 H (6-7)36486048164 sum140 110 beta From the Central Limit Theorem, project completion time is normally distributed with a mean of 140 days and a standard deviation of/


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