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    Learning

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    behaviour = classical conditioning ( conditioned stimuli) & instrumental learning ( discriminative stimuli) Stimulus Control - Habit Learning Motivation = Why individuals initiate choose or persist in specific actions in specific circumstance Motivation is transient / temporary fluctuation of state ( learning is enduring ) and it is energizing Motivation vs Learning: Car analogy - motivation translates learning into action 4 Theories on Motivation: Instinct Theory How

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    Data Research

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    Data Collection: Data collection is the heart of any research. No study is complete without the data collection. This research also includes data collection and was done differently for different type of data. TYPES OF DATA Primary Data: For the purpose of collecting maximum primary data‚ a structured questionnaire was used wherein questions pertaining to the satisfaction level of the customer about pantaloons product(apparel)‚ the quality‚ color‚ variety of products‚ the availability of different

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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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    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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    Lab – Solubility Curve for Potassium Nitrate I. Purpose: To plot a solubility curve for potassium nitrate in aqueous solution. II. Safety: Wear eye protection throughout the experiment. Be cautious of hot water. Wash hands if you come into contact with potassium nitrate. III. Procedure: 1. Carefully weigh out your assigned mass of potassium nitrate on a piece of weigh paper. Carefully pour the sample into the large test tube. 2. Add 20. mL of distilled water to the tube.

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    Kfc Data

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    KFC * 1. INTRODUCTION …… KFC is the world’s No.1 Chicken QSR. Leading in UK‚ Australia‚ South Africa‚ China‚USA‚ Malaysia and many more. Introduced many offerings for its growing customer base in India while staying rooted in the taste legacy of Colonel Harland Sander’s secret recipe. Products are made on the motto of“Crispy outside‚ juicy inside” . In India‚ KFC is growing rapidly and today has presence in 21 * 2. HISTORICALBACKGROUND…… In 1930’s Colonel Harland Sanders some distinguished

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    Executive Summary Peachtree is a successful and well-established healthcare organization with several institutions including hospitals‚ medical schools and clinics. With their broad market they have reached‚ they need to work on their IT system as the current system consumed so much maintenance time. They have several different incompatible IT systems in their organization amongst the institutions. Therefore Peachtree is looking or a reliable IT system to achieve their objective which is delivering

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    Data Leakage Detection

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    KNOWLEDGE AND DATA ENGINEERING‚ VOL. 23‚ NO. 1‚ JANUARY 2011 51 Data Leakage Detection Panagiotis Papadimitriou‚ Student Member‚ IEEE‚ and Hector Garcia-Molina‚ Member‚ IEEE Abstract—We study the following problem: A data distributor has given sensitive data to a set of supposedly trusted agents (third parties). Some of the data are leaked and found in an unauthorized place (e.g.‚ on the web or somebody’s laptop). The distributor must assess the likelihood that the leaked data came from

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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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