August 2012 This month’s newsletter is the first in a multi-part series on using the ANOVA method for an ANOVA Gage R&R study. This method simply uses analysis of variance to analyze the results of a gage R&R study instead of the classical average and range method. The two methods do not generate the same results, but they will (in most cases) be similar. This newsletter focuses on part of the ANOVA table and how it is developed for the Gage R &R study. In particular it focuses on the sum of squares and degrees of freedom. Many people do not understand how the calculations work and the information that is contained in the sum of squares and the degrees of freedom. In the next few issues, we will put together the rest of the ANOVA table and complete the Gage R&R calculations. In this issue: Sources of Variation Example Data The ANOVA Table for Gage R&R The ANOVA Results Total Sum of Squares and Degrees of Freedom Operator Sum of Squares and Degrees of Freedom Parts Sum of Squares and Degrees of Freedom Equipment (Within) Sum of Squares and Degrees of Freedom Interaction Sum of Squares and Degrees of Freedom Summary Quick Links Sources of Variation Any gage R&R study is a study of variation. This means you have to have variation in the results. On occasion, I get a phone call from a customer wondering why their Gage R&R study is not giving them any useful information. And, in looking at the results, I discover that each result is the same – for each part and for each operator. There is no variation. I am asked – Isn’t it good that there is no variation in the results? No, not in a gage R&R study. It means that...

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August 2012 This month’s newsletter is the first in a multi-part series on using the ANOVA method for an ANOVA Gage R&R study. This method simply uses analysis of variance to analyze the results of a gage R&R study instead of the classical average and range method. The two methods do not generate the same results, but they will (in most cases) be similar. This newsletter focuses on part of the ANOVA table and how it is developed for the Gage R &R study. In particular it focuses on the sum of squares and degrees of freedom. Many people do not understand how the calculations work and the information that is contained in the sum of squares and the degrees of freedom. In the next few issues, we will put together the rest of the ANOVA table and complete the Gage R&R calculations. In this issue: Sources of Variation Example Data The ANOVA Table for Gage R&R The ANOVA Results Total Sum of Squares and Degrees of Freedom Operator Sum of Squares and Degrees of Freedom Parts Sum of Squares and Degrees of Freedom Equipment (Within) Sum of Squares and Degrees of Freedom Interaction Sum of Squares and Degrees of Freedom Summary Quick Links Sources of Variation Any gage R&R study is a study of variation. This means you have to have variation in the results. On occasion, I get a phone call from a customer wondering why their Gage R&R study is not giving them any useful information. And, in looking at the results, I discover that each result is the same – for each part and for each operator. There is no variation. I am asked – Isn’t it good that there is no variation in the results? No, not in a gage R&R study. It means that...

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MySQL by Examples for Beginners Read “How to Install MySQL and Get Started” on how to install, customize, and get started with MySQL. 1. Summary of MySQL Commands Used in this Tutorial For detailed syntax, check MySQL manual “SQL Statement Syntax” @ http://dev.mysql.com/doc/refman/5.5/en/sql-syntax.html. — Database-Level DROP DATABASE databaseName — Delete the database (irrecoverable!) DROP DATABASE IF EXISTS databaseName — Delete if it exists CREATE DATABASE databaseName — Create a new database CREATE DATABASE IF NOT EXISTS databaseName — Create only if it does not exists SHOW DATABASES — Show all the databases in this server USE databaseName — Set the default (current) database SELECT DATABASE() — Show the default database SHOW CREATE DATABASE databaseName — Show the CREATE DATABASE statement — Table-Level DROP TABLE [IF EXISTS] tableName, … CREATE TABLE [IF NOT EXISTS] tableName ( columnName columnType columnAttribute, … PRIMARY KEY(columnName), FOREIGN KEY (columnNmae) REFERENCES tableName (columnNmae) ) SHOW TABLES — Show all the tables in the default database DESCRIBE|DESC tableName — Describe the details for a table ALTER TABLE tableName … — Modify a table, e.g., ADD COLUMN and DROP COLUMN ALTER TABLE tableName ADD columnDefinition ALTER TABLE tableName DROP columnName ALTER TABLE tableName ADD FOREIGN KEY (columnNmae) REFERENCES tableName (columnNmae) ALTER TABLE tableName DROP FOREIGN KEY constraintName SHOW CREATE TABLE tableName — Show the CREATE TABLE statement for this tableName — Row-Level INSERT INTO tableName VALUES (column1Value, column2Value,…) — Insert on all Columns INSERT INTO tableName VALUES (column1Value, column2Value,…), … — Insert multiple rows INSERT INTO tableName (column1Name, …, columnNName) VALUES (column1Value, …, columnNValue) — Insert on selected Columns DELETE FROM tableName WHERE criteria UPDATE tableName SET columnName = expr, … WHERE criteria SELECT * | column1Name AS alias1, …, columnNName AS aliasN FROM tableName WHERE criteria GROUP BY columnName ORDER BY columnName ASC|DESC, … HAVING groupConstraints LIMIT count |...

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A STUDY ON CUSTOMER SATISFACTION TOWARDS VARIOUS SOFTWARE PRODUCTS OFFERED BY YAMEE CLUSTER ABSTRACT This study On Study on Customer Satisfaction Level towards Various Products Offered by YAMEE CLUSTER, Chennai” aims at the understanding customer satisfaction of the product and YAMEE CLUSTER services and analyze the customer coverage area of that product. Primary data were collected with the help of the structured questionnaire from the existing customers of this concern. The sample size considered for the study was 100 where YAMEE CLUSTER. The tools for the analysis include Frequency analysis, Data reduction analysis, Cross tabulation, Chi-square test, weighted average analysis. And dependent on reasonable price and product advantage and after sales services is concern price. TABLE OF CONTENTS Chapter No. Title Page No. I Introduction 1.1 Introduction 10 1.2 Industry Profile 11 1.3 Company Profile 13 1.4 Review of Literature 21 II Main Theme of the Research 2 3 2.1 Objectives 24 2.2 Research Methodology 25 III Data Presentation & Analysis 26 3.1 Frequency table 27 3.2 Cross table 45 3.3 Chi-square testing 47 3.4 Anova testing 48 IV Findings & Suggestions 49 4.1 Findings 50 4.3 Conclusion 51 Bibliography 52 Questionnaire 53 LIST OF TABLES Serial No. Title Page No. 1 Frequency Table for service provider 27 2 Frequency Table for company automated 28 3 Frequency Table for manual process still exit after computerization 29 4 Frequency Table for gaps for automated 29 5 Frequency Table for process changes 30 6 Frequency Table 31 7 Frequency Table for have any...

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09NN01 OPTIMIZATION TECHNIQUES 3 1 0 4 LINEAR PROGRAMMING: Linear Programming: Graphical method, Simplex method, Revised simplex method, Duality in linear programming (LP), Sensitivity analysis, other algorithms for solving LP problems, Transportation, assignment and other applications. (9) NON-LINEAR PROGRAMMING: Non Linear Programming: Unconstrained optimization techniques, Direct search methods, Descent methods, constrained optimization. (9) INTEGER AND DYNAMIC PROGRAMMING: Formulation of Integer Programming Problems, Gomory’s cutting plane methods, Branch and Bound Techniques, Characteristics of Dynamic programming, Bellman’s principle of optimality, Concepts of dynamic method of solution. (11) PERT/CPM: Network Construction-Computation of earliest start time, latest start time, total, free and independent float time -Crashing – Computation of optimistic, most likely, pessimistic and expected time- Resource analysis in Network scheduling. (5) NON TRADITIONAL TECHNIQUES: Genetic Algorithm, Simulated Annealing, Tabu Search and Neural Networks. (8) Total 42 REFERENCES: Rao S S, “Engineering Optimization: Theory and Practice”, New Age International, New Delhi, 2006. Trivedi K S, “Probability and Statistics with Reliability, Queuing and Computer Applications”, Prentice Hall, New Delhi, 2006. Taha H A, “Operations Research: An Introduction”, Pearson Education, New Delhi, 2006. Alberto Leon–Garcia, “Probability and Random Processes for Electrical Engineering”, Pearson Education, New Delhi, 2007. Jang J S R, Sun C T and Mizutani, “Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence”, Pearson Education, New Delhi, 2005. 09NN02 DATA STRUCTURES AND ALGORITHMS 3 1 0 4 INTRODUCTION: Primitive Data Types – Abstract Data types – Algorithm Analysis – Time and Space Complexity. (3) LISTS: Arrays – Linked Lists – Stacks and Queues – Applications – Implementation of Recursive Functions. (5) TREES: Binary Trees – Tree Traversals – Binary Search Trees – Balanced Search Trees – AVL – Red-Black...

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