Using the Standard Deviation You made a number of observations about the data sets for the school activities. You used mean and median to measure the center of the data‚ and you used the interquartile range (IQR) to measure the spread. When outliers are present‚ the median and IQR are used to measure center and spread because they are unaffected by extreme values. When the data appears to be symmetric and there are no known outliers‚ the mean and standard deviation (another measure of spread)
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COURSE NO | SUBJECT | FACULTY | CC09 | FINANCIAL MANAGEMEMENT | Ms. AMBILI JOSE | CC10 | MARKETING MANAGEMENT | Mr. SREENATH .R | CC11 | HUMAN RESOURCE MANAGEMENT | Dr. ANU GEORGE | CC12 | OPERATIONS MANAGEMENT | Mr. NIBU RAJ ABRAHAM | CC13 | ENVIRONMENT MANAGEMENT | Mr. GIJO GEORGE | CC14 | OPERATIONS RESEARCH | Ms. AMBILI JOSE | CC15 | RESEARCH METHODOLOGY | Mr. JEFFIN JOHN | CC16 | MANAGEMENT INFORMATION SYSTEMS | Mr. MIDHUN JOSE | CC17 | VIVA- VOCE | | CC09 -FINANCIAL
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Big Data‚ Data Mining and Business Intelligence Techniques 2 What is Data? • Data is information in a form suitable for use with a computer. • There are two types of data ▫ Structured ▫ Unstructured • The total volume of data is growing 59% every year. • The number of files grow at 88% every year. 3 What is Big Data? Exa Analytics on Big Data at Rest Up to 10‚000 Times larger Peta Data Scale Giga Data at Rest Tera Data Scale Mega Traditional Data Warehouse
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the following terms: a. In what type of regression is it likely to occur? b. What is bad about autocorrelation in a regression? c. What method is used to determine if it exists? (Think of statistical test to be used) d. If found in a regression how is it eliminated? Problem 8 Define Multicollinearity in the following terms: a. In what type of regression is it likely to occur? b. Why is multicollinearity in a regression a difficulty to be resolved? c. How
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Components of DSS (Decision Support System) Data Store – The DSS Database Data Extraction and Filtering End-User Query Tool End User Presentation Tools Operational Stored in Normalized Relational Database Support transactions that represent daily operations (Not Query Friendly) Differences with DSS 3 Main Differences Time Span Granularity Dimensionality Operational DSS Time span Real time Historic Current transaction Short time frame Long time frame Specific Data facts Patterns Granularity Specific
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Lecture Notes 1 Data Modeling ADBMS Lecture Notes 1: Prepared by Engr. Cherryl D. Cordova‚ MSIT 1 • Database: A collection of related data. • Data: Known facts that can be recorded and have an implicit meaning. – An integrated collection of more-or-less permanent data. • Mini-world: Some part of the real world about which data is stored in a database. For example‚ student grades and transcripts at a university. • Database Management System (DBMS): A software package/ system to facilitate
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Stock Exchange forecasting with Data Mining and Text Mining (Marketing and Sales Analysis) Full names : Fahed Yoseph TITLE : Senior software and Database Consultatnt (Founder of Info Technology System) E-mail: Yoseph@info-technology.net Date of submission: Sep 15th of 2013 CONTENTS PAGE Chapter 1 1. ABSTRACT 2 2. INTRODUCTION 3 2.1 The research problem. 4 2.2 The objectives of the proposal. 4 2.3 The Stock Market movement. 5 2.4 Research question(s). 6 2. Background 3. Problem
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for67757_fm.fm Page i Saturday‚ January 7‚ 2006 12:00 AM DATA COMMUNICATIONS AND NETWORKING for67757_fm.fm Page ii Saturday‚ January 7‚ 2006 12:00 AM McGraw-Hill Forouzan Networking Series Titles by Behrouz A. Forouzan: Data Communications and Networking TCP/IP Protocol Suite Local Area Networks Business Data Communications for67757_fm.fm Page iii Saturday‚ January 7‚ 2006 12:00 AM DATA COMMUNICATIONS AND NETWORKING Fourth Edition Behrouz A. Forouzan DeAnza College with
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doctor has charted Dexter’s mass and related it to his BMI (Body Mass Index). A BMI between 20 and 26 is considered healthy. The data is shown in the following table. Mass(kg)62 72 66 79 85 82 92 88 BMI 19 22 20 24 26 25 28 27 (a) Create a scatter plot for the data. (b) Describe any trends in the data. Explain. (c) Construct a median–median line for the data. Write a question that requires the median– median line to make a prediction. (d) Determine the equation of the median–median line
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university CASE STUDY OF DATA MINING Summitted by Jatin Sharma Roll no -32. Reg. no 10802192 A case study in Data Warehousing and Data mining Using the SAS System. Data Warehouses The drop in price of data storage has given companies willing to make the investment a tremendous resource: Data about their customers
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