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    Data Description

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    Chapter 3 Data Description 3-1 Measures of Central Tendency ( page 3-3) Measures found using data values from the entire population are called: parameter Measures found using data values from samples are called: statistic A parameter is a characteristic or measure obtained using data values from a specific population. A statistic is a characteristic or measure obtained using data values from a specific sample. The Measures of Central Tendency are: • The Mean • The

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    Statistics and Data

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    Definition: Statistics is the study of the collection‚ organization‚ analysis‚ interpretation and presentation of data. It deals with all aspects of this‚ including the planning of data collection in terms of the design of surveys and experiments. A statistician is someone who is particularly well-versed in the ways of thinking necessary for the successful application of statistical analysis. Such people have often gained experience through working in any of a wide number of fields. Some

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    data communication

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    What is Data Communications? Next Topic | TOC The distance over which data moves within a computer may vary from a few thousandths of an inch‚ as is the case within a single IC chip‚ to as much as several feet along the backplane of the main circuit board. Over such small distances‚ digital data may be transmitted as direct‚ two-level electrical signals over simple copper conductors. Except for the fastest computers‚ circuit designers are not very concerned about the shape of the conductor or

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    Data mining

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    Data Mining Project – Dogs Race Prediction Motivation Gambling is very popular in the Republic of Ireland‚ weather is online or not‚ more people are joining gambling communities formed all over the Island of Ireland. The majority of these communities are involved in horse races related gambling and other sports‚ but there is a significant amount of people dedicated to dogs races. This is a multimillion Euro industry developed on-line and live or face to face. Objective There are many websites

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    Data Anomalies

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    Data Anomalies Normalization is the process of splitting relations into well-structured relations that allow users to inset‚ delete‚ and update tuples without introducing database inconsistencies. Without normalization many problems can occur when trying to load an integrated conceptual model into the DBMS. These problems arise from relations that are generated directly from user views are called anomalies. There are three types of anomalies: update‚ deletion and insertion anomalies. An update anomaly

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    Secondary Data

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    1 Secondary data analysis: an introduction All data are the consequence of one person asking questions of someone else. (Jacob 1984: 43) This chapter introduces the field of secondary data analysis. It begins by considering what it is that we mean by secondary data analysis‚ before describing the type of data that might lend itself to secondary analysis and the ways in which the approach has developed as a research tool in social and educational research. The second part of the chapter considers

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    Data processing

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    2 Areas of data processing 1. Business Data processing (BDP) . Business data processing is characterized by the need to establish‚ retain‚ and process files of data for producing useful information. Generally‚ it involves a large volume of input data‚ limited arithmetical operations‚ and a relatively large volume of output. For example‚ a large retail store must maintain a record for each customer who purchases on account‚ update the balance owned on each account‚ and a periodically present a

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    Data Security

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    Module 5 Data Security What is a computer security risk? A computer security risk is any event or action that could cause loss of or damage to computer hardware‚ software‚ data‚ information‚ or processing capability. Some breaches to computer security are accidental‚ others are planned intrusions. Some intruders do no damage; they merely access data‚ information or programs on the computer before logging off. Other intruders indicate some evidence of their presence either by leaving a

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    Data Structures

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    EE2410: Data Structures Cheng-Wen Wu Spring 2000 cww@ee.nthu.edu.tw http://larc.ee.nthu.edu.tw/˜cww/n/241 Class Hours: W5W6R6 (Rm 208‚ EECS Bldg) Requirements The prerequites for the course are EE 2310 & EE 2320‚ i.e.‚ Computer Programming (I) & (II). I assume that you have been familiar with the C programming language. Knowing at least one of C++ and Java is recommended. Course Contents 1. Introduction to algorithms [W.5‚S.2] 2. Recursion [W.7‚S.14] 3. Elementary data structures: stacks‚ queues

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    Data Analysis

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    c. ratio scale d. interval scale 2. Data obtained from a nominal scale a. must be alphabetic b. can be either numeric or nonnumeric c. must be numeric d. must rank order the data 3. In a post office‚ the mailboxes are numbered from 1 to 4‚500. These numbers represent a. qualitative data b. quantitative data c. either qualitative or quantitative data d. since the numbers are sequential‚ the data is quantitative 4. A tabular summary of a set of data showing the fraction of the total number

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