...Multivariate Discriminant Analysis Priyanshi Gupta An Overview MDA is a statistical technique used to classify an observation into one of the several a priori groupings dependent on the observation’s individual characteristics. It is used primarily to classify and/or make predictions in the problems where dependent variable comes in qualitative form, for example, male or female, bankrupt or non-bankrupt etc. So the first step is to establish explicit group classifications. We have got observations coming from k groups. We are trying to look at what is the best way or best function in order to discriminate observations coming from different groups. Once such function is in place, we go to classification which basically is the problem of classification of a new observation into appropriate population using the discriminant function. So typically in such problems, once you have a set of data (called LEARNING set of data) with observations possibly coming from different populations are pre-classified, having predefined memberships to the groups. And based on the particular previously classified data, we create a discriminant function and can use it after proper calibration to classify a new observation to be coming from one of the groups. Discriminant analysis is used when groups are known a priori. Types of DA Problems 2 Group Problems... …regression can be used k-Group Problem (where k>=2)... …regression cannot be used if k>2 Example of a...
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... Multivariate data is a key part of any interaction in business. The data can be used to anticipate the effect of several variables. Multivariate relationships involve multiple independent variables affecting a dependent variable. These independent variables have a distinct and measurable effect on the dependent variable. These relationships can be used by managers to make decisions. The example given is that of an automobile manufacturer that uses the data to change the methods of scheduled maintenance without affecting the longevity of the vehicle. Multivariate data can show managers how different aspects can affect an outcome. Multivariate Data Multivariate data is a system of relationships that governs nearly any interactions between objects. These data relationships show how one set of variables can have an effect on another. Whenever something happens, it happens because of many factors that come into play; several things have to come together to create the effect observed. This is true of things in nature, occurrences in life, and decisions in business. Multivariate relationships are everywhere, and the effect they have is widespread. The ability to recognize and analyze these variables can be a strong asset in business management as understanding what drives certain effects can allow a manager to more accurately predict outcomes. Being able to accurately model what is going to happen is a distinct advantage for any manager. Multivariate relationships...
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...Analytics Concepts and Definitions Types of Analytics Descriptive Analytics: * Post Event Analytics * Add features to website and measure its effectiveness in form of clicks, link sharing, page views * Descriptive Analytics Tools -> Google Analytics, Optimizely Diagnostic Analytics: * Post Event Analytics * Analytics used to diagnose why something/phenomenon happened the way it did * It basically provides a very good understanding of a limited piece of the problem you want to solve. * Usually less than 10% of companies surveyed do this on occasion and less than 5% do so consistently. Predictive Analytics: * Used for Prediction of Phenomenon using past and current data statistics * Essentially, you can predict what will happen if you keep things as they are. * However, less than 1% of companies surveyed have tried this yet. The ones who have, found incredible results that have already made a big difference in their business. * Eg:- SAS, RapidMiner, Statistica Prescriptive Analytics: * Prescriptive analytics automatically synthesizes big data, multiple disciplines of mathematical sciences and computational sciences, and business rules, to make predictions and then suggests decision options to take advantage of the predictions. * It is considered final phase of Analytics Some Analytics Techniques used Linear Regression In statistics, linear regression is an approach for modeling the relationship between a scalar...
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...Bivariate statistical tests are nothing but a kind of statistical analysis. Such process incorporates two variables signified by X, Y in most of the cases. The purpose of these kinds of tests is to determine the empirical relationship between two different variables. This is better to see those variables are interrelated or not. A common part such kind of analysis is to find out whether those two variables are changeable in response to each and every measure or not. Such change happens simultaneously. This kind of data analysis process is useful enough to test hypotheses of association and causality. It helps to verify how it is easy to predict the easiness and prediction of the value in terms of dependent variable in case of a known case value of an independent variable. These kinds of statistical tests can be contrasted with some univariate analysis. In this case, only single variable can be analyzed. The purpose is to describe in this case. Subgroup comparison that is nothing but a process of analysis in descriptive kind between two variables is a very simple form of bivariate analysis. This is a process to analyze two different variables. ------------------------------------------------- Types of Bivariate Statistical Tests: A very usual form of bivariate analysis is to create percentage table along with a scatterplot graph. Even it includes the calculation of a simple correlation coefficient. To give an instance, such tests tend to investigate the significant zone of men...
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...Q.M SAMENVATTING: Chapter 2: Observation: A single member of a collection of items that we want to study such as person, firm or a region. Variable: A characteristic of the subject or individual, such as an eployee’s income or an invoice amount. Data set: Consists all the values of all of the variables for all the observations we have chosen to observe. Univariate data set: one variable. Bivariate data set: two variables. Multivariate data set: More than two variables. Data Types: Categorical Data: Have values that are describes by words rather than numbers. Verbal Label: Example – Vechile Type (Car, Truck, SUV). Coded(Binary): Example – Vechile type (1, 2, 3). Numerical Data: Arise from counting, measuring something, or some kind of mathematical operation. Discrete: Example – Broken eggs in a carton (1,2,3,4…N). Continuous: Example – Patient waiting...
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...UNIVERSITY OF ENGINEERING AND TECHNOLOGY, TAXILA FACULTY OF TELECOMMUNICATION AND INFORMATION ENGINEERING COMPUTER ENGINEERING DEPARTMENT Digital Image Processing Lab Manual No 03 Dated: 31st August, 2015 to 04th September, 2015 Semester: Autumn 2015 Digital Image Processing Session:-2012 Computer Lab Instructor:-Engr. Farwa UNIVERSITY OF ENGINEERING AND TECHNOLOGY, TAXILA FACULTY OF TELECOMMUNICATION AND INFORMATION ENGINEERING COMPUTER ENGINEERING DEPARTMENT Objectives:The objectives of this session is to understand following. Image Resizing Image Interpolation Relationships between pixels Distance Transform Image Resizing:Resizing an image consists of enlarging or shrinking it, using nearest-neighbor, bilinear, or bicubic interpolation. Both resizing procedures can be executed using the imresize function. Let us first explore enlarging an image. Enlarge the cameraman image by a scale factor of 3. By default, the function uses bicubic interpolation. I=imread('cameraman.tif'); I_big1 = imresize(I,3); figure, imshow(I), title(’Original Image’); figure, imshow(I_big1), interpolation’); title(‘Enlarged Image using bicubic Use the imtool function to inspect the resized image, I_big1. Scale the image again using nearest-neighbor and bilinear interpolations. I_big2 = imresize(I,3,’nearest’); I_big3 = imresize(I,3,’bilinear’); figure, imshow(I_big2),title(‘Resized interpolation’); figure, imshow(I_big3)...
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...Factor-analyzing Likert-scale data under the assumption of multivariate normality complicates a meaningful comparison of observed groups or latent classes Gitta Lubke University of California, Los Angeles Bengt Muth´n e University of California, Los Angeles Abstract Treating Likert scale data as continuous outcomes in confirmatory factor analysis violates the assumption of multivariate normality. Given certain requirements pertaining to the number of categories, skewness, size of the factor loadings, etc., it seems nevertheless possible to recover true parameter values if the data stem from a single homogenous population. It is shown in a multi-group and a latent class context that analyzing Likert data under the assumption of multi-variate normality may distort the factor structure differently across groups or classes. Hence, investigating measurement invariance, which is a necessary requirement for a meaningful comparison of observed groups or latent classes, is problematic. Analyzing subscale scores computed from Likert items does not necessarily solve the problem. Based on a power study, some conditions are established to obtain acceptable results. Questionnaires designed to measure latent variables such as personality factors or attitudes typically use Likert scales as a response format. In response to statements such as ‘does the student yell at others’, participants are asked to choose one of a given number of ordered response categories which run for instance from...
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...instance where by we monitor two related quality characteristics is necessary in multivariate control chart. It is recommended to use a multivariate analysis, monitoring two quality characteristic can be misleading. For instance monitoring a frequency of two radio X1 and X2 respectively the control chart can indicate being under the expected control limit ,when we run the two frequency simultaneously under the multivariate chart . Commonly occurring result is revealed, however using multivariate control chart to analyse two or more related quality characteristic the confidence interval level is high if the point accurately plotted 2. Describe briefly how a multivariate normal distribution is used as a model for multivariate process data. The multivariate normal distribution are closely related to univariate normal distribution . The multivariate normal distribution is the random vector (X1, X2…..Xk) = X. The multivariate normal distribution is defined by a vector of means µ1, µ2……µn (where ‘n’ is the constant number of variable) and the variance-covariance matrix∑. It is an extension of the univariate normal distribution for applications with a group of variables that may be correlated or with related characteristics. Suppose there are ‘J’ variables denoted with Xi…Xj. the mean of each variable is µi……µj, the vector of the Mean µ ′=[µi……µj] while the variance is σi,….. σj Multivariate normal probability function is expressed as: F(x) = 1 (2) 2 |∑|1/2−1 2(−µ)′ ∑−1...
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...Package ‘norm’ February 20, 2015 Version 1.0-9.5 Date 2013/02/27 Title Analysis of multivariate normal datasets with missing values Author Ported to R by Alvaro A. Novo . Original by Joseph L. Schafer . Maintainer John Fox Description Analysis of multivariate normal datasets with missing values License file LICENSE URL http://www.stat.psu.edu/~jls/misoftwa.html#aut Repository CRAN Repository/R-Forge/Project norm Repository/R-Forge/Revision 8 Repository/R-Forge/DateTimeStamp 2013-02-27 16:01:38 Date/Publication 2013-02-28 07:11:32 NeedsCompilation yes License_restricts_use no R topics documented: .code.to.na . . . . .na.to.snglcode . da.norm . . . . . em.norm . . . . . getparam.norm . imp.norm . . . . loglik.norm . . . logpost.norm . . makeparam.norm mda.norm . . . . mdata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...
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...Marketing badge 2009 Personality : Quick learner, emotion stability, quick adaptable, kind hearted. 38 APPENDIX A Original journal Consumer decision-making styles on domestic and imported brand clothing The Authors Cheng-Lu Wang, Department of Marketing & International Business, University of New Haven, West Haven, USA Noel Y.M. Siu, Department of Marketing, Hong Kong Baptist University, Kowloon, Hong Kong Alice S.Y. Hui, Department of Marketing, Hong Kong Baptist University, Kowloon, Hong Kong Abstract The relationship between consumers’ decision-making styles and their choice between domestic and imported brand clothing is investigated using a sample of Chinese consumers. The multivariate analysis of variance and discriminant analysis results indicate that seven decision-making styles together with other consumer behavioural characteristics can be used to distinguish and profile consumers who prefer to buy domestic, imported or both types of clothing. Empirical findings reveal that consumers who prefer to buy imported brand clothing tend to have a unique lifestyle and shopping orientation that differ from those who prefer domestic brand clothing. Conceptual contributions and managerial implications are discussed. Keyword(s): Consumers; Decision making; Brands; Clothing; China; Consumer behaviour; Market segmentation. Article Type: Research Paper, Journal: European Journal of Marketing, Volume: 38, Number: ½, Year: 2004, pp: 239-252...
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...|Effectiveness analysis of an IMC plan – analysis on Djuice. | |Research Report | | | | | Table of Contents Contents Executive Summary 3 Background 4 Statement of the Problem 11 Approach to the Problem 12 Research Design 14 Data Analysis 15 Results 16 Limitation and Caveats 21 Conclusion and Recommendations 22 Exhibit 23 Reference 27 Executive Summary I am going to conduct a research project on “Effectiveness analysis of an IMC plan – analysis on DJUICE”. Integrated marketing communication is integration of all marketing tools, approaches, and resources within a company which maximizes impact on consumer mind and which results into maximum profit at minimum cost. It aims to ensure consistency of message and the complementary use of media. To be an Effective brand IMC plan plays a major role. Effectiveness of a brand is measured by consumer preference. Advertising and other promotional tools, Word of Mouth, Service quality, Tariff and Offer etc are the variables for preferring a mobile phone. The objective is to identify the effect of each factor on preference of Djuice. This research project could act as the guideline to estimate what the Djuice users expect from the company and what influence...
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...This study examines the applicability of theory of reasoned action (TRA) in a context of Islamic pawnshop using structural equation modelling (SEM). The present study presents a simplified theory of TRA, hence it is intended to test whether the two constructs in the theory are acceptable or not in a newly context of Islamic pawnshop. The simplified theory is tested using survey data from 250 respondents. Out of these, only 221 questionnaires are found to be usable whilst the rest are omitted owing to the incomplete responses. The results reveal that attitude was significantly related to the intention to use Islamic pawnshop. Subjective norm was also significantly associated with the intention to use Islamic pawnshop. In sum, the present study provides us valuable insights for service providers to future planning of Islamic pawnshop businesses. Key words: Attitude, subjective norm, questionnaire-survey, theory testing, Islamic pawnshop, Malaysia. INTRODUCTION In this study, an Islamic pawn is normally noted as arRahnu. Ar-Rahnu is defined as a method of providing short-term financing to a person by pawning her jewellery to banks or pawnshops as a security. It is one of the micro financing facilities available for low and middle class income earners who are seeking financial assistance to meet two purposes. Firstly, for precautionary purposes, mainly when one encounters unexpected situations such as death and accident which required quick cash, and less cumbersome financial assistance...
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...Cluster Analysis1 Cluster analysis, like reduced space analysis (factor analysis), is concerned with data matrices in which the variables have not been partitioned beforehand into criterion versus predictor subsets. In reduced space analysis our interest centers on reducing the variable space to a smaller number of orthogonal dimensions, which maintains most of the information–metric or ordinal– contained in the original data matrix. Emphasis is placed on the variables rather than on the subjects (rows) of the data matrix. In contrast, cluster analysis is concerned with the similarity of the subjects–that is, the resemblance of their profiles over the whole set of variables. These variables may be the original set or may consist of a representation of them in reduced space (i.e., factor scores). In either case the objective of cluster analysis is to find similar groups of subjects, where “similarity” between each pair of subjects is usually construed to mean some global measure over the whole set of characteristics–either original variables or derived coordinates, if preceded by a reduced space analysis. In this section we discuss various methods of clustering and the key role that distance functions play as measures of the proximity of pairs of points. We first discuss the fundamentals of cluster analysis in terms of major questions concerning choice of proximity measure, choice of clustering technique, and descriptive measures by which the resultant clusters can...
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...QA Concept Introducing LoadRunner • Why should you automate performance testing? • What are the LoadRunner components? • Understanding LoadRunner Terminology • What is the load testing process? • Getting Familiar with HP Web Tours • Application Requirements The Power of LoadRunner • Creating the Load Test • Running the Load Test • Monitoring the Load Test • Analyzing Results Building Scripts • Introducing the Virtual User Generator (VuGen) • How do I start recording user activities? • Using VuGen’s Wizard mode • How do I record a business process to create a script? • How do I view the script? Playing Back Your Script • How do I set the run-time behavior? • How do I watch my script running in real time? • Where can I view information about the replay? • How do I know if my test passed? • How do I search or filter the results? Solving Common Playback Problems • Preparing HP Web tours for playback errors • How do I work with unique server values? Preparing a Script for Load Testing • How do I measure business processes? • How do I emulate multiple users? • How do I verify Web page content? • How can I produce debugging information? • Did my test succeed? Creating a Load Testing Scenario • Introducing the LoadRunner Controller • How do I start the Controller? • The Controller window at a glance • How do I modify the script details? • How do I generate a heavy load? • How do I emulate real load...
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...Preparing Business Scenario Analyses The following general guidelines may be used in preparing for an oral or written business scenario analysis and presentation. There may be several feasible courses of action regarding the solution to any case. It is more important to concern yourself with the process of problem definition and isolation, analysis, and evaluation of alternatives, and the choice of one or more recommendations, rather than trying to find a single answer. Very often, the right answer is the one that you can propose, explain, defend, and make work. • The Process of Analyzing a Case 1. Read and study the scenario thoroughly and efficiently. Read the scenario once for familiarity, noting issues that come to the forefront. Read the scenario again. Determine all the facts, making notes about symptoms of problems, root problems, unresolved issues, and roles of key players. Watch for issues beneath the surface. 2. Isolate the problem(s). Get a feel for the overall environment by putting yourself in the position of one of the key players. Seek out the pertinent issues and problems. 3. Analyze and evaluate alternatives. a. Once the problems and issues are isolated, work at gaining a better understanding of causes. In what area of the unit do the problems exist? Why? What caused them? Examine and evaluate the strengths and weaknesses of the unit’s processes (e.g., planning, communication), human behaviors, and/or exhibits (e.g., financial statements,...
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