IE 305 Laboratory M_1 Introduction to the Metrology Laboratory Section 008 November 14‚ 2012 Introduction The first Metrology lab’s main purpose was to help each team familiarize themselves with the Metrology lab workstation and the apparatus involved. Teams were introduced to new instruments‚ like the Micrometer‚ Depth Micrometer‚ Vernier Caliper‚ Vernier Height Gage and Gage Blocks. The teams were then briefed about each instrument and how each tool has is
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IT433 Data Warehousing and Data Mining — Data Preprocessing — 1 Data Preprocessing • Why preprocess the data? • Descriptive data summarization • Data cleaning • Data integration and transformation • Data reduction • Discretization and concept hierarchy generation • Summary 2 Why Data Preprocessing? • Data in the real world is dirty – incomplete: lacking attribute values‚ lacking certain attributes of interest‚ or containing only aggregate data • e.g.‚ occupation=“ ”
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Concept Application of Concept in Scenario Reference to Concept in Reading Identify tools of data analysis Descriptive and inferential statistics: Descriptive statistics allow for gathering and presenting the information in a meaningful way. A good example of the use of descriptive statistics is the initial demographic profiling of target cities in the Coffee Time simulation. In the quest for market expansion of the Coffee Time in South Asian marketplace‚ Brad Collins of Coffee Time has profiled
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UNIVERSITY OF OSLO Department of Informatics in collaboration with OSLO UNIVERSITY COLLEGE Department of Computer Science MASTER THESIS ADMINISTRATION OF REMOTE COMPUTER NETWORKS Stig Jarle Fjeldbo May 23‚ 2005 Abstract Today’s computer networks have gone from typically being a small local area network‚ to wide area networks‚ where users and servers are interconnected with each other from all over the world. This development has gradually expanded as bandwidth has become
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Statistics - Final Project Table of contents 1.0 Introduction 3 1.1 The aim of the study 3 2.0 Methodology 3 2.1 Correlation 4 2.2 Independent Samples Test 4 2.3 Two Way Anova 4 2.4 One Sample T-Test 4 2.5 Regression 5 2.6 Histogram 5 3.0 Data Analysis 5 3.1 Question 2 5 3.2 Question 3 6 3.3 Question 4 7 3.4 Question 5 8 3.5 Question 6 8 3.6 Question 7 10 4.0 Conclusions 11 5.0 References 12 1.0 Introduction A company with factories located in
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Predicting the compressive and tensile strength of rocks from indentation hardness index by S. Kahraman*‚ M. Fener†‚ and E. Kozman‡ Synopsis The prediction of rock properties from indirect testing methods is important‚ particularly for preliminary investigations since indirect tests are easier and cheaper than the direct tests. In this study‚ we investigate the predictability of the uniaxial compressive strength (UCS ) and Brazilian tensile strength (BTS ) of rocks from the indentation hardness
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Statistics – Lab Week 2 Name:Michael Jacks Math221 Statistical Concepts: * Using MINITAB * Graphics * Shapes of Distributions * Descriptive Statistics * Empirical Rule Data in MINITAB * MINITAB is a powerful‚ yet user-friendly‚ data analysis software package. You can launch MINITAB by finding the icon and double clicking on it. After a moment you will see two windows‚ the Session Window in the top half of the screen and the Worksheet or Data Window in the bottom
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International Journal for the Scholarship of Teaching and Learning http://www.georgiasouthern.edu/ijsotl Vol. 2‚ No. 2 (July 2008) ISSN 1931-4744 @ Georgia Southern University The Impact of Grading on the Curve: A Simulation Analysis George Kulick Le Moyne College Syracuse‚ New York‚ USA kulick@lemoyne.edu Ronald Wright Le Moyne College Syracuse‚ New York‚ USA wright@lemoyne.edu Abstract Grading on the curve is a common practice in higher education. While there are many critics of the practice
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8 5 3 0 150 155 160 165 170 175 180 185 190 2. List data in order 157‚160‚162‚165‚166‚167‚167‚167‚168‚169‚170‚171‚172‚173‚174‚174‚176‚178‚180‚182 3. What’s the Range of data: Range=Highest-Lowest=182-157=25(cm) 4. Plot the data in a Histogram and describe the distribution 7 Frequency 5 4 2 0 157.5 162.5 167.5 172.5 177.5 182.5 Heights The distribution is symmetrical. It has a normal shape. It has a slight skew to the right. 2015/6/2 !1 GAC010 AE2 Louis ID Cumulative
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Labs Lab 1: Learning @Risk Basics In this lab you will create and run a Sales spreadsheet model. Please take your time on this lab; even though it is easy‚ it is your one guided opportunity to learn how to use the @Risk software. Launch @Risk from the Start menu. Begin by building the following spreadsheet: 1 2 A Sales Forecast C D E Sales Dollars in Millions 3 4 5 6 7 8 9 B Customers Big Guy Little Guy New Guy Zero-One Guy Total Sales This Year $25
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