'Data value' presentation slideshows

Descriptive vs. Inferential Statistics

Descriptive vs. Inferential Statistics. Descriptive Methods for summarizing data Summaries usually consist of graphs and numerical summaries of the data Inferential Methods of making decisions or predictions about a populations based on sample information. Data Vocabulary.

5.3k views • 134 slides

Chapter 4

Chapter 4. Capturing the Requirements Shari L. Pfleeger Joanne M. Atlee 4 th Edition. Contents. 4.1 The Requirements Process 4.2 Requirements Elicitation 4.4 Types of Requirements 4.4 Characteristic of Requirements 4.5 Modeling Notations

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Planning a software project: Function point analysis.

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Chapter 4

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Python Programing: An Introduction to Computer Science

Python Programing: An Introduction to Computer Science. Chapter 11 Data Collections. Objectives. To understand the use of lists (arrays) to represent a collection of related data. To be familiar with the functions and methods available for manipulating Python lists.

1.14k views • 80 slides

Chapter A: Network Model

Chapter A: Network Model Basic Concepts Data-Structure Diagrams The DBTG CODASYL Model DBTG Data-Retrieval Facility DBTG Update Facility DBTG Set-Processing Facility Mapping of Networks to Files Basic Concepts Data are represented by collections of records .

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Chapter 3: Diagnostics and Remedial Measures

Chapter 3: Diagnostics and Remedial Measures. Ayona Chatterjee Spring 2008 Math 4813/5813. Validity of a regression model. Any one of the following features may not be appropriate. Linearity Normality of error terms. Important to examine the aptness of a model before making inferences.

1.69k views • 47 slides

Chapter 2: Algorithm Discovery and Design

Chapter 2: Algorithm Discovery and Design. Invitation to Computer Science, Java Version, Second Edition. Objectives. In this chapter, you will learn about: Representing algorithms Examples of algorithmic problem solving. Introduction.

735 views • 39 slides

Chapter 3 Numerically Summarizing Data

Chapter 3 Numerically Summarizing Data. 3.3 Measures of Central Tendency and Dispersion from Grouped Data. EXAMPLE Approximating the Mean from a Frequency Distribution.

498 views • 37 slides

2.3 Describing Distributions Numerically

529 views • 36 slides

Statistics

Statistics. Chapter 10. 10.1 Organizing and Picturing Information. Line Plot: A line plot is a basic and intuitive visual representation of data. 16. 14. 12. 10. Frequency. 8. 6. 4. 2. 10. 20. 30. 40. 50. 60. 70. 80. 90. 100. Student Test Scores.

554 views • 35 slides

Security in Operating Systems

Security in Operating Systems. Cuiwei Zhao. Security in Operating System. Security breaches Security goals Protected objects of the general purpose operating system Protection of objects . Beaches. Exposure A form of possible loss or harm in a computing system Vulnerability

686 views • 30 slides

C++ Pointers

C++ Pointers. Joseph Spring/Bob Dickerson School of Computer Science Operating Systems and Computer Networks Based on notes by Bob Dickerson and Introduction to C++ Programming by Roberge and Smith. Areas for Discussion. Pointers: Introduction Declaring Pointers Dereferencing a Pointer

840 views • 24 slides

Workshop Agenda

Workshop Agenda. METEOROLOGICAL DATA. 2-2. Meteorological Data. Introduction. HYSPLIT requires at a minimum, u,v,w,T,P sfc , and H if on pressure surfaces in three dimensions (RH or Q are optional) Meteorological data are specially formatted to minimize execution time Compressed binary

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Pipelining

CDA 3101 Discussion Section 11. Pipelining. Question 1. Suppose that time for an ALU operation can be shortened by 25% in the following figure a. Will it affect the speedup obtained from pipelining? If yes, by how much? If no, why? b. What if the ALU operation now takes 25% more time?.

1.2k views • 17 slides

Statistics Unit 2: Organizing Data

Statistics Unit 2: Organizing Data. Ms. Hernandez St. Pius X High School 2006-2007. Saying it with Pictures. Organizing Data Graphic Summaries Show data Encourage reader focus on data Yet, avoid distorting what data have to say See examples on pg 40 compare/contrast. Basic Graphs.

331 views • 17 slides

Amarillo by Morning: Data Visualization in Geostatistics

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STEM-AND-LEAF DISPLAYS

STEM-AND-LEAF DISPLAYS. Uses actual data values to create a graph. STEPS: 1. Select a typical data value. 2. Divide this number into leading and trailing digits. 3. Write down all possible sets of leading digits in order from the lowest to highest as a column. These are stems.

289 views • 9 slides