PPT-Slide 1 5-3 GRAPH FREQUENCY DISTRIBUTIONS

Create a frequency distribution from a set of data Use boxandwhisker plots and stemandleaf plots to display information OBJECTIVES Slide 2 frequency . Remember to Silence Your Cell . Phone and Put It In Your Bag!. Introduction. Statistics. Descriptive Statistics. Inferential Statistics. Graphical Displays of Data. Make the display user friendly. Title. 1. CUSSing. & BS-. ing. practice. Center:. This distribution of quiz scores appears to have two modes, one at around 55, and another at around 80.. Shape:. The shape is bimodal, and around each mode the shape is roughly symmetric.. Fred Davies. ASTR 278. 2/23/12. Contents. Eddington Ratio. What does it mean?. How do we measure it?. Contents. Eddington Ratio. What does it mean?. How do we measure it?. Two regimes of measurement. AS91586 Apply probability distributions in solving problems. NZC level 8. Investigate situations that involve elements of chance. calculating and interpreting expected values and standard deviations of discrete random variables. Percentiles and Percentile Ranks and their Graphical Representations. Note: we’ll be skipping book sections:. 2.4 (apparent and real limits) . 2.8, 2.9 (percentile and percentile ranks for grouped data). Objective. : . To solve multistep probability tasks with the concept of geometric distributions. CHS Statistics. A . Geometric probability model. . tells us the probability for a random variable that counts the number of . A Brief Introduction. Random Variables. Random Variable (RV): A numeric outcome that results from an experiment. For each element of an experiment’s sample space, the random variable can take on exactly one value. Measure description:. The . Government will introduce a specific measure preventing the distribution of franking credits where a distribution to shareholders is funded by particular capital raising activities. . Maryam . Aliakbarpour. (MIT). Joint work with: Eric . Blais. (U Waterloo) and . Ronitt. . Rubinfeld. (MIT and TAU). 1. The Problem . 2. R. elevant features in distributions.  . Smokes. Does not regularly exercise . Maryam . Aliakbarpour. (MIT). Joint work with: Eric . Blais. (U Waterloo) and . Ronitt. . Rubinfeld. (MIT and TAU). 1. The Problem . 2. R. elevant features.  . Smokes. Does not regularly exercise . Hao Wei. 1. , . Jeffrey Xu Yu. 1. , Can L. u. 1. , . Xuemin. Lin. 2. . 1 . The . Chinese University of Hong Kong, Hong Kong. 2 . The . University of New South Wales. , . Sydney, Australia. Graph in Big Data . of . Graph Streams. Arijit Khan. Charu. C. Aggarwal. Nanyang Technical University. Singapore. IBM. T. J. Watson Research Lab. NY,. USA. Graph Streams. 1. /23. Graph Stream:. Continuous stream of graph edges . Adjacency List. Adjacency-Matrix. Pointers/memory for each node (actually a form of adjacency list). Adjacency List. List of pointers for each vertex. Undirected Adjacency List. Adjacency List. The sum of the lengths of the adjacency lists is 2|E| in an undirected graph, and |E| in a directed graph.. 18. O AT 35 MEV/NUCLEON ON . 9. BE AND . 181. TA TARGETS. Erdemchimeg. Batchuluun. 1,2. , A.G Artukh. 1. , S.A Klygin. 1. , G.A Kononenko. 1. , . Yu.M. . Sereda. 1. , A.N. Vorontsov. 1. T.I, Mikhailova.