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Antecedents and consequences of apparel involvement
Antecedents and consequences of apparel involvement

... relevant product to student respondents. Some previously studied apparel categories in involvement research were jeans, dresses, bras, socks, and T-shirts. However, little work has focused thoroughly on apparel involvement. There were several studies that used the concept of apparel involvement in r ...
Real business cycles: A Reader
Real business cycles: A Reader

... G.King and Charles I.Plosser for “Real Business Cycles and the Test of the Adelmans” in Journal of Monetary Economics, vol. 33:2, April 1989, pp. 405–438. Louisiana State University Press and James E.Hartley, Kevin D.Salyer and Steven M.Sheffrin for “Calibration and Real Business Cycle Models: An Un ...
Executive Summary - EDA Incubator Tool
Executive Summary - EDA Incubator Tool

... Association, and Cybergroup Inc. – used a robust methodology to collect and statistically analyze data, and determine specific relationships between how an incubation program operates and how its client companies perform, as measured by a number of outcomes. The purpose of this study is to test whet ...
Customer Loyalty Attributes: A Perspective
Customer Loyalty Attributes: A Perspective

... Oliver (1999) also suggested that action loyalty is perceived as a necessary result of engaging previous phases of loyalty and is accompanied by an additional desire to overcome obstacles that may prevent a customer from patronizing the service organization. McMullan (2005) presented studies concer ...
Link (PDF, 5.57 MB) (PDF, 5441 KB)
Link (PDF, 5.57 MB) (PDF, 5441 KB)

... 3.4.5 Expected value problem . . . . . . . . . . . . . . . . . . . 3.4.6 Equivalent LP . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.7 Current solver availability . . . . . . . . . . . . . . . . . . 3.5 SLP models with a joint integrated chance constraint . . . . . . . 3.5.1 Modeling losses ...
Learning Latent Sentiment Scopes for Entity
Learning Latent Sentiment Scopes for Entity

... the collapsed CRF, we integrate both named entity information and sentiment level information together to form label sequences. We extend such an approach by using 9 different types of nodes at each word/position, namely B+ , E+ and A+ nodes for positive sentiment, B− , E− and A− nodes for negative ...
Explanatory Variable/Error Term Independence Premise
Explanatory Variable/Error Term Independence Premise

... o Can the calculations for the standard errors be trusted? o Is the ordinary least square (OLS) estimation procedure for the coefficient value the most reliable, the best linear unbiased estimation procedure (BLUE)? In the previous two chapters we showed that the violation the first two standard ord ...
SAP BusinessObjects Business Intelligence with SAP
SAP BusinessObjects Business Intelligence with SAP

... BI is being deployed in the company. SAP’s tools for reporting, dashboarding, analysis and planning can all access HANA, but in technically different ways. The Self Service BI product SAP Lumira is an example of how SAP is taking its first steps towards tighter integration between HANA and BusinessO ...
The subsistence to commercial transition in agricultural development
The subsistence to commercial transition in agricultural development

... Coward, Elisha Walter Jr., "The subsistence to commercial transition in agricultural development" (1969). Retrospective Theses and ...
New Measures of Clumpiness for Incidence Data Yao Zhang Eric T. Bradlow
New Measures of Clumpiness for Incidence Data Yao Zhang Eric T. Bradlow

... does not provide persuasive evidence of “hot hands”. Their studies have been widely cited, leading many to believe that the perception by fans and players that athletes sometimes get hot is an example of how people erroneously see patterns in random data. An important question to be asked is whether ...
Cusum - Stata
Cusum - Stata

... The resulting plot, which is U-shaped, suggests a negative monotonic relationship. The trend is confirmed by a highly significant linear cusum statistic, labeled CusumL in the output above. Some 29.73% of the cars are foreign (coded 1). The proportion of foreign cars diminishes with increasing weigh ...
3839grading3840 - Emerson Statistics
3839grading3840 - Emerson Statistics

... f. In parts a-d of this problem, we described the distribution of death within 5 years across groups defined by LDL level. What if we fit a logistic regression model mimicking the approach used in problems 1 – 4 of homework #2, where we described the distribution of LDL across groups defined by vita ...
Advertising Theories and Models œ how well can these be
Advertising Theories and Models œ how well can these be

... hope to send a message to their customers that later on will result in the customer buying their products or services (Dahlqvist & Linde, 2002). External Communications are important on the market today and it can be hard to find new ways to compete on. It is therefore very essential for organisatio ...
Allocation of Marketing Resources to Optimize Customer Equity
Allocation of Marketing Resources to Optimize Customer Equity

... Once the financial profile of customers has been estimated, the use of risk management techniques to select the optimal customer portfolio is addressed in chapter 4. I discuss methods for the allocation of a limited marketing budget by formulating and solving the associated constrained optimization ...
Academic Script
Academic Script

... is also a super key. But in some situations, no proper subset is a super key. Those minimal super keys are called candidate keys. Assume that a combination of student_name and student_street identifies the members of the student entity set. Then { student_id } and { student_name, student_street } bo ...
mcq regression and correlation with correct
mcq regression and correlation with correct

... A measure of the strength of the linear relationship that exists between two variables is called: (a) Slope (b) Intercept (c) Correlation coefficient (d) Regression equation MCQ 14.42 When the ratio of variations in the related variables is constant, it is called: (a) Linear correlation (b) Nonlinea ...
Data mining of temporal sequences for the prediction of infrequent
Data mining of temporal sequences for the prediction of infrequent

... or failures either prior or directly upon their occurrence. Diagnosis is a term which englobes at the same time the observation of a situation (monitoring of an industrial system) and the relevant decisions to be taken following this observation (system degraded or not, etc.). It is a vast research ...
Data mining of temporal sequences for the prediction of infrequent
Data mining of temporal sequences for the prediction of infrequent

... or failures either prior or directly upon their occurrence. Diagnosis is a term which englobes at the same time the observation of a situation (monitoring of an industrial system) and the relevant decisions to be taken following this observation (system degraded or not, etc.). It is a vast research ...
The Customer Marketing Database: Cutting Costs and Improving
The Customer Marketing Database: Cutting Costs and Improving

... Slow response time can indicate different things, depending on the question. For example, it may be necessary to know how many customers answered a survey question a certain way. A properly designed and well maintained customer marketing database should be able to deliver this answer within seconds. ...
PDF
PDF

... phenomenon in aggregate time-series models. However, at the household level, it complicates the study because of the non-negativity restriction on household purchases.2 Further, the temporal linkage of purchasing in panel data models, unlike in aggregate models, arises not only from state dependence ...
Lecture 14: Generative Models
Lecture 14: Generative Models

... IML Lecture 14 ...
Scribe Notes
Scribe Notes

... of parameters is itself considered to be a random variable. One example is to do clustering with k-means (or mixture of Gassuians) while the number of clusters k is unknown. Bayesian inference addresses this problem by treating k itself as a random variable. A prior is defined over an infinite dimen ...
Fuzzy-Mapping-Rules
Fuzzy-Mapping-Rules

... The centroid defuzzification is then applied to the rules with crisp consequents with the following formula: y = ∑Mi=1 wici / ∑Mi=1 wi where wi is the degree to which ith rule matches the input data. This method reduces the computation cost and facilitates the application of neural networks learning ...
Service Marketers
Service Marketers

... service, widening the zone of tolerance. ...
Portfolio Value-at-Risk Using Regular Vine Copulas
Portfolio Value-at-Risk Using Regular Vine Copulas

... equity price or commodity price fluctuations, additionally risks occurring due to investments that cannot be traded fast enough in order to minimize/prevent losses (liquidity risk). Furthermore, credit risk is the risk of not receiving promised payments, such as repayment of loan or other obligation ...
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Predictive analytics

Predictive analytics encompasses a variety of statistical techniques from modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future, or otherwise unknown, events.In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision making for candidate transactions.The defining functional effect of these technical approaches is that predictive analytics provides a predictive score (probability) for each individual (customer, employee, healthcare patient, product SKU, vehicle, component, machine, or other organizational unit) in order to determine, inform, or influence organizational processes that pertain across large numbers of individuals, such as in marketing, credit risk assessment, fraud detection, manufacturing, healthcare, and government operations including law enforcement.Predictive analytics is used in actuarial science, marketing, financial services, insurance, telecommunications, retail, travel, healthcare, pharmaceuticals, capacity planning and other fields.One of the most well known applications is credit scoring, which is used throughout financial services. Scoring models process a customer's credit history, loan application, customer data, etc., in order to rank-order individuals by their likelihood of making future credit payments on time.
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