'Linear data' diaporamas de présentation

Graphs Arrays Iteration Combining Data Structures

Lecture 13. Graphs Arrays Iteration Combining Data Structures. Lecture 13. Graphs. The Scenario. Imagine we need to represent a highway system within our algorithm. Chattanooga. Greenville. Atlanta. Birmingham. Tampa. Which Data Structure?. Arrays are fixed in size and linear

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Statistics of the CMB

Statistics of the CMB. From Boltzmann equation of photons to power spectra 2-point statistics on the sphere: ML, quadratic estimators, polarisation-specifics. Outline of the lectures. Boltzmann equation of CMB. FLAT SCLICING GAUGE. NEWTONIAN GAUGE. Homogeneous solution.

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Support Vector Machines

Support Vector Machines. Jordan Smith MUMT 611 14 February 2008. Topics to cover. What do Support Vector Machines (SVMS) do? How do SVMs work? Linear data Non-linear data (Kernel functions) Unseparable data (added Cost function) Search optimization Why?. What SVMs do. What SVMs do.

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CS5540: Computational Techniques for Analyzing Clinical Data

CS5540: Computational Techniques for Analyzing Clinical Data. Prof. Ramin Zabih (CS) Prof. Ashish Raj (Radiology) Today only: Gary Dorfman, MD (Radiology). What is CS5540 about?. The practice of medicine is being transformed by computation

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GIS Rollup and Drilldown: Geospatial BI and Data Aggregation

GIS Rollup and Drilldown: Geospatial BI and Data Aggregation. Brian Hebert ScribeKey, LLC May, 2011. Presentation Overview. Look at geospatial data compilation and viewing using Business Intelligence (BI) and Data Warehousing approaches.

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ACT 1-on-1 Curriculum Math: Pre-algebra & Algebra

ACT 1-on-1 Curriculum Math: Pre-algebra & Algebra. ACT Math. Math Section at a Glance. 60 questions 60 minutes Three sections of questions: Pre-Algebra/ Elementary Algebra 24 questions Intermediate Algebra/ Coordinate Geometry 18 questions Plane Geometry/ Trigonometry

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Automated Patch Generation

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Transformations

Transformations. Transformation (re-expression) of a Variable. Transformation of a variable can change its distribution from a skewed distribution to a normal distribution (bell-shaped, symmetric about its centre. A very useful transformation is the natural log transformation.

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Linear Transformations 2.1b

Linear Transformations 2.1b. Target Goal: I can transform linear data and determine its effect on center and spread. HW: pg 107: 19-21, 23. Changing the Unit of Measurement. Linear Transformations C hanges the original variable x into the new variable x new given by an equation of the form:

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DO NOW

DO NOW. EXPLAIN YOUR ANSWER!. Stats: Modeling the World. Chapter 7 Scatterplots, Association, and Correlation. Making a Picture of Bivariate Data. Relationships between variables are often what we truly want to know about our data.

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Presenter: Mathias Jahnke Authors: M. Zhang, M. Mustafa, F. Schimandl*, and L. Meng

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The Design and Analysis of Algorithms

The Design and Analysis of Algorithms. by Anany Levitin Lecture notes prepared by Lydia Sinapova, Simpson College. CHAPTER 1: INTRODUCTION. What is an Algorithm Steps in Designing and Implementing an Algorithm Important Problem Types Fundamental Data Structures. ALGORITHM.

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AP Statistics

AP Statistics. 4.1 Modeling Nonlinear Data. Learning Objective. Create scatter plots of non linear data Transform nonlinear data to use for prediction Create residual plots. Exponential Function: Power function:. Exponential Growth. To show exponential growth:

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F 3 @ T 3 (Function Family Fun @ T 3 )

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AP Statistics

AP Statistics. 4.1 Modeling Nonlinear Data. Learning Objective. Create scatter plots of non linear data Transform nonlinear data to use for prediction. Exponential Function: Power function:. Exponential Growth. To show exponential growth: We look for a common ratio

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Linear and Non-Linear Curves

Linear and Non-Linear Curves. Linear Data. Given a set of 2-variable data, the first logical thing to do, is to look at a scatter-plot of the data points. (2 nd ,Y=, Plot 1, ON, Scatter-plot, L1, L2, Zoom Stat(#9))

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Residuals and Residual Plots Most likely a linear regression will not fit the data perfectly.

Residuals and Residual Plots Most likely a linear regression will not fit the data perfectly. The residual (e) for each data point is the ________________________ from the data point to the regression line. It is the error in __________________.

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