Editing shapes in your presentation - PowerPoint In this article Introduction Changing Shapes Edit Points to Create Virtually Anything WordArt as a Powerful Design Tool Ungrouping Clip Art to Create Custom Graphics Introduction For a human being to change can take years of study, education, or travel. Shapes, on the other hand, have it easy. Okay, so shapes don’t have it quite that easy. Change your shapes whenever and however you please. Changing Shapes There are three ways to change the shape of a built-in shape. Many shapes have one or more reshape tools (the yellow diamond that appears on the perimeter of the shape when selected) that you can drag to alter the shape. Some reshape tools handle fairly basic changes, such as the depth or thickness of an arrow or the size of a pie slice. Similarly, the 4-Part Star becomes an octagon when its reshape tool is moved as far as it can go. Some reshape tools also provide useful details that you might not know are available to you. Edit Points to Create Virtually Anything About the author
Integrate external tools and builders in Eclipse Before you start About this tutorial One of the nice things about an IDE is that you can do the majority of your development tasks in one application — hence, integrated. It takes time away from your work to have to switch from your IDE to another program. The more often you have to switch among applications, the more you have, well, just a DE. That's not much of a step above a text editor. Using launch configurations in Eclipse Europa, you can run external programs from within the development environment. This tutorial introduces two main scenarios: One uses the venerable Apache Ant build tool that comes with Eclipse; the other is an example of a script that executes from within the IDE. Objectives In this tutorial, you learn how to build and use launch configurations. System requirements To get the most out of this tutorial, all you need is Eclipse Europa. And before you get started, add a new Java™ project to an Eclipse workspace and put at least one Java file in the project. Back to top
Mrp example - example of material requirement planning Next, it is going to be exposed the practical case of the operating MRP, we will see again the making up of the scissors; remember the bill of materials (BOM) is the following: To a better understanding of the MRP, imagine you need 2 screws to make the scissor , therefore the bill of materials will be the following The Master Production Schedule shows we have to make 400 scissors during the 3 rd week, in the 4 th week 600, in the 6 th week 800, and in the 7 th week 300 scissors. We will name (GR) Gross Requirements to the demand of fabrication of the products, the final products (in this case the scissors) correspond to the quantity appeared in the MPS. To the intermediate products (in this case the screws) you have to multiply the necessary quantity to make the final product with its demand. The file indicated us that we have since the first week 550 scissors in stock, also it indicates that the security stock do not have to be less than 50 scissors. Net Requirements of the MRP
Choice Based Conjoint Analysis - Enterprise Feedback Management - Market Research Software Here are some simple steps to assimilate the information before beginning your online conjoint survey. Attributes: Define the attributes for your market segment. For most studies, try to keep the number of attributes below five. If you have a large number of attributes, try aggregating and combining these attributes into meaningful composite attributes. Levels: Define at least two levels for each of the attributes. Minimal Respondent Base: Try to figure out if your respondent base is homogeneous. Minimal Choice Count for statistical validity: Try to come up with a minimum number of times a Level should be shown to the respondents to make a statistically valid sampling. You do not need to come up with both the Minimal Respondent Base and Minimal Choice Count.
SPSS Conjoint Understand and measure purchasing decisions IBM® SPSS® Conjoint helps market researchers increase their understanding of consumer preferences so they can more effectively design, price and market successful products. It enables them to model the consumer decision-making process so they can design products with the features and attributes most important to their target market. SPSS Conjoint includes procedures that can help researchers: Design an orthogonal array of product attribute combinations using ORTHOPLAN, a design generator. The output for the orthogonal design has one row for each profile, with the factors displayed as columns. This table gives the predicted probabilities of choosing each of the simulation cases as the most preferred one, under three different probability-of-choice models. The real power of conjoint analysis is the ability to predict preference for product profiles that weren't rated by the subjects. SPSS Conjoint Screenshots More Less Produce and print cards
What is Choice Based Conjoint (CBC)? What is Choice-Based Conjoint? Choice-Based Conjoint (CBC) is used for discrete choice modeling, now the most often used conjoint-related method in the world. The main characteristic distinguishing choice-based from other types of conjoint analysis is that the respondent expresses preferences by choosing from sets of concepts, rather than by rating or ranking them. The choice-based task is similar to what buyers actually do in the marketplace. Choosing a preferred product from a group of products is a simple and natural task that everyone can understand. If you are having trouble deciding which conjoint method might be best for your specific situation, try our Interactive Advisor. CBC is often used to study the relationship between price and demand. The CBC System provides everything needed to conduct a choice-based conjoint study via Web, CAPI (devices not connected to the Web), or paper-based surveys. CBC data can be analyzed in a number of ways.
What is Conjoint Value Analysis (CVA)? Overview Conjoint Value Analysis (CVA) is a legacy conjoint analysis software based on the original ratings-based conjoint approach from the 1970s. It is not often used today (~2% of total conjoint analysis projects as reported by Sawtooth Software users), but has some advantages for certain research situations. CVA can display either one or two products at a time. It may be useful for both product design and pricing research, when the number of attributes is about six or fewer. CVA includes an excellent designer for traditional conjoint analysis that some users employ for general design of experiments or for specific applications such as MBC. Before choosing the CVA system for your project, we recommend you discuss your research needs with a Sawtooth Software content expert to determine that it would be appropriate. CVA System Up to 30 attributesUp to 15 levels per attribute (text and/or graphics) Methodology CVA's Designer helps you create the conjoint interview.
Discrete Choice Methods with Simulation, by Kenneth Train, Cambridge University Press, 2002 The 2nd Edition files provided here are all in PDF format. The files vary in size from 60 KB to 238 KB. This electronic version of Discrete Choice Methods with Simulation is made available for use by individuals for their personal research and study. Permission is not granted to use any part of this work for any other purpose whatsoever without the express written consent of the Cambridge University Press. Front Material and Quotations on Jacket Chapter 1. A zip archive is available for download (2 MB) , containing all the chapters. For first-edition readers: Errata discovered in the first edition (corrected in second edition.)
Logit and Probit Models Chapter Logit, Nested Logit, and Probit models are used to model a relationship between a dependent variable Y and one or more independent variables X. The dependent variable, Y, is a discrete variable that represents a choice, or category, from a set of mutually exclusive choices or categories. For instance, an analyst may wish to model the choice of automobile purchase (from a set of vehicle classes), the choice of travel mode (walk, transit, rail, auto, etc.), the manner of an automobile collision (rollover, rear-end, sideswipe, etc.), or residential location choice (high-density, suburban, exurban, etc.). Examples: An analyst wants to model: 1. 2. 3. 4. 1) The observations on dependent variable Y are assumed to have been randomly sampled from the population of interest (even for stratified samples or choice-based samples). 2) Y is caused by or associated with the X’s, and the X’s are determined by influences (variables) ‘outside’ of the model. Pavements Traffic Planning McFadden, Daniel.
Nature of the chi-square distribution This activity has been undergone anonymous peer review. This activity was anonymously reviewed by educators with appropriate statistics background according to the CAUSE review criteria for its pedagogic collection. This page first made public: May 17, 2007 This material is replicated on a number of sites as part of the SERC Pedagogic Service Project Summary In this activity, students learn the true nature of the chi-square and F distributions in lecture notes (PowerPoint file) and an Excel simulation. In a subsequent activity, instances of when the chi-square and F-distributions are related to the normal or t-distributions (e.g. link text(Microsoft Word 3.5MB May17 07) Learning Goals In this first activity students will learn the important lesson that statistical distributions, such as the normal, Student's t, chi-square and F distributions are interrelated. Context for Use The activity can be undertaken at different levels and with different degrees of rigor. Teaching Notes and Tips