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Spreadsheets and Business Graphics: Facts and Figures

Spreadsheets and Business Graphics: Facts and Figures

Spreadsheets and Business Graphics: Facts and Figures Chapter 13 Objectives Describe the advantages of spreadsheets List several applications for spreadsheets Explain the underlying principles of electronic spreadsheet use Describe how to set up and modify a spreadsheet

By paul2
(278 views)

Displaying Statistical Information

Displaying Statistical Information

Displaying Statistical Information Statistical Information may be displayed: As a table As a chart As a graph Frequency Table Pie Chart Histogram Frequency Distribution Graph Cumulative Frequency Distribution Graph Constructing a Frequency Table

By niveditha
(567 views)

GIS Data and Data Sources DEM, TIGER, DLG, LULC, Soils, DOQQ, DRG

GIS Data and Data Sources DEM, TIGER, DLG, LULC, Soils, DOQQ, DRG

GIS Data and Data Sources DEM, TIGER, DLG, LULC, Soils, DOQQ, DRG. 1. Digital Elevation Models (DEM). The elevation value are stored as a matrix of regularly spaced ground positions Each data point represents the elevation of the grid cell in which it is located. 1. DEM 1:24,000 .

By bernad
(442 views)

Pennsylvania’s Home Visitation Stakeholder Committee Meeting February 20, 2013 Harrisburg, PA

Pennsylvania’s Home Visitation Stakeholder Committee Meeting February 20, 2013 Harrisburg, PA

Pennsylvania’s Home Visitation Stakeholder Committee Meeting February 20, 2013 Harrisburg, PA. Introductions. Work Groups. In Your Work Group. Please take notes!. At 11:15. Be ready to share your sequence of next steps Be ready to respond to questions or suggestions from the group.

By yeva
(275 views)

Virginia Syndromic Surveillance Advisory Group (VSSAG) Pilot Kick-Off Call

Virginia Syndromic Surveillance Advisory Group (VSSAG) Pilot Kick-Off Call

Virginia Syndromic Surveillance Advisory Group (VSSAG) Pilot Kick-Off Call. January 9, 2019. Agenda. Pilot Goals Pilot Expectations Pilot Role-Based Access NSSP ESSENCE Demo Confirm Paperwork/NSSP Accounts/Data Access. Pilot Goals. Grant NSSP data access to pilot VSSAG members

By kendall
(177 views)

Revision 2 October 26 th , 2005

Revision 2 October 26 th , 2005

Revision 2 October 26 th , 2005. Enervista Use Enervista to Simplify Your Workflow Process. Industry-Leading Suite of Software Tools to Manage your Relays and Meters. EnerVista Suite. Easy to Use Monitoring and Data Recording for Small Systems Pre-configured screens for Instant Monitoring

By zalika
(141 views)

Computer Graphics and Image Processing (CIS-601)

Computer Graphics and Image Processing (CIS-601)

Computer Graphics and Image Processing (CIS-601). Image Segmentation by Clustering (Using Mahalanobis Distance). - Manjit Chintapalli. What is Image Segmentation ?. To humans, an image is not just a random collection of pixels; it is a meaningful arrangement of regions and objects.

By Albert_Lan
(256 views)

Face detection and recognition

Face detection and recognition

Face detection and recognition. Many slides adapted from K. Grauman and D. Lowe. Face detection and recognition. Detection. Recognition. “Sally”. Consumer application: iPhoto 2009. http://www.apple.com/ilife/iphoto/. Consumer application: iPhoto 2009. Can be trained to recognize pets!.

By tal
(162 views)

Inference on a Single Mean

Inference on a Single Mean

Inference on a Single Mean. Use Calculation from Sample to Estimate Population Parameter. (select). Population. Sample. (calculate). (describes). (estimate). Parameter. Statistic. Use Calculation from Sample to Estimate Population Parameter. (select). Population. Sample. (calculate).

By dasha
(249 views)

Federal Grants and the Academic Pipeline: White Paper Figures

Federal Grants and the Academic Pipeline: White Paper Figures

Federal Grants and the Academic Pipeline: White Paper Figures. Figure 1: Basic & Applied Research Funds Awarded by U.S. Federal Agencies to Universities and Colleges (2000 constant $*). Estimated 2009 Increase** (Including Stimulus Funds). Billions of $ (2000 constant).

By nova
(104 views)

Prediction and Accuracy

Prediction and Accuracy

Prediction and Accuracy. Lesson 3.5. The median-median line method gives you a process by which anyone will get the same line of fit for a set of data.

By lin
(270 views)

Defining the target behavior

Defining the target behavior

Teachers often feel that they only have 4 options in dealing with behaviors. Ignore the problem and hope it goes away Refer the student/s to an assistant principal Call the students’ parents and ask for help Apply a consequence, such as detention or some other punishment.

By deion
(105 views)

به نام خدا

به نام خدا

به نام خدا. 1. Atomic Force Microscop (AFM). History and Definitions in Scanning Probe Microscopy (SPM) History Scanning Tunneling Microscope (STM) Developed in 1982 by Binning, Rohrer, Gerber, and Weibel at IBM in Zurich, Switzerland.

By awen
(276 views)

Nonlinear Regression

Nonlinear Regression

Nonlinear Regression. Electrical Engineering Majors Authors: Autar Kaw, Luke Snyder http://numericalmethods.eng.usf.edu Transforming Numerical Methods Education for STEM Undergraduates. Nonlinear Regression http://numericalmethods.eng.usf.edu. Nonlinear Regression.

By lois
(146 views)

1	Operations Strategy 2	Process Analysis 3	Lean Operations 4	Supply Chain Management Class 5a: Inventories & Economi

1 Operations Strategy 2 Process Analysis 3 Lean Operations 4 Supply Chain Management Class 5a: Inventories & Economi

Operations Management & Performance Modeling. 1 Operations Strategy 2 Process Analysis 3 Lean Operations 4 Supply Chain Management Class 5a: Inventories & Economies of Scale Class 5b: Dealing with Uncertainty & role of Centralization Class 6a: Supply Chain Design

By lefty
(199 views)

Coevolution: Studying the Dynamics of Competition and Gauss: Scenario Translation

Coevolution: Studying the Dynamics of Competition and Gauss: Scenario Translation

Coevolution: Studying the Dynamics of Competition and Gauss: Scenario Translation. Mary McDonald Science Applications International Corporation. What is “coevolution”? . A term originally from Evolutionary Biology

By aizza
(175 views)

Clustering 101

Clustering 101

Clustering 101. Ka Yee Yeung Center for Expression Arrays University of Washington. Overview. What is clustering? Similarity/distance metrics Hierarchical clustering algorithms Made popular by Stanford, ie. [Eisen et al . 1998] K-means

By adair
(165 views)

Part IV: Inference algorithms

Part IV: Inference algorithms

Part IV: Inference algorithms. Estimation and inference. Actually working with probabilistic models requires solving some difficult computational problems… Two key problems: estimating parameters in models with latent variables

By abrial
(163 views)

Source Control and Stormwater at Sediment Sites

Source Control and Stormwater at Sediment Sites

Source Control and Stormwater at Sediment Sites. Warren Hansen, PE Windward Environmental LLC. Challenges and complexities of controlling stormwater sources. Thinking in the context of SCALE. Stormwater and other pathways. Stormwater as One of Several Pathways.

By mariah
(127 views)

unsupervised learning - clustering

unsupervised learning - clustering

Inter-cluster distances are maximized. Intra-cluster distances are minimized. unsupervised learning - clustering. Finding groups of objects such that the objects in a group will be similar (or related) to one another and different from (or unrelated to) the objects in other groups.

By tilden
(504 views)

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