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IS 335 Chapter 5 Practice Questions

Quiz yourself by thinking what should be in each of the black spaces below before clicking on it to display the answer.
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Question
Answer
show Process of creating new insights from information  
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show data, statistical and quantitative analysis, explanatory and predictive modeling, and fact based decision making.  
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show 1) Discovery by using existing information about how decisions are made in order to build systems that make similar decisions 2) Discovery by finding useful patterns in observations, typically embodied in explicit data  
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Define Knowledge Discovery in Databases:   show
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show Process of applying statistical or intelligent algorithms. Viewed as involving the whole business intelligence process, whereas DM may refer more specifically to the actual application of DM techniques  
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What are the factors that drive BI?   show
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Exploding data volumes is important because...   show
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Increasing decision complexity results from...   show
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show Efficiently and effectively to environmental changes, with greater accountability for their actions.  
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show Electronic Retailer Ex) eBags, a web based storefront of handbags, suitcases, wallets, and other similar products  
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show 1) Banking 2) Target Marketing 3) Insurance 4) Telecommunications 5) Operations Management 6) Retail Sales Forecasting 7) System Diagnosis  
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show A set of specifications. Industry-neutral and tool-neutral process for data mining that provides a hierarchical process model for defining the basic tasks of the BI process.  
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show Process of cleaning and transforming raw data prior to processing and analysis. Important step prior to processing and often involves reformatting data, making corrections to data and the combining of data sets to enrich data.  
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What are the tasks for Data preparation?   show
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Step 1 (Selection) for Data Preparation involves...   show
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Step 2 (Construction and Transformation of Variables) for Data Preparation involves...   show
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show Dataset required by the DM model may reside on multiple disjoint databases, which will need to be consolidated for the model to use. Data consolidation may require redefinition of some of the data fields to allow for consistency.  
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show Involves the reformatting and reordering of the data fields, as required by the DM model.  
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Define Model Building and Validation:   show
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show N-Fold Cross Validation, specifically, ten-fold validation  
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Define Ten-Fold Validation:   show
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show Next task in the DM process (after Model Building and Validation), validation dataset is fed through the model. Model outcomes or predicted results can be compared with the actual outcomes in the validation dataset.  
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show Last task in the DM process, task implements the live DM model within an organization to aid the decision-making process. A valid model must make sense, and a pilot implementation is always required prior to live deployment  
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What is the CRISP-DM Methodology?   show
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show 50-80%  
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Define Data Collection:   show
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Define Data Description:   show
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Define Data Quality and Verification:   show
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What is the first step in order to define which DM technique to use...   show
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show Understand the characteristics of the input and outcome data that will be used.  
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Define "To Describe What Happened":   show
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show Look for patterns in prior activities or actions that affect these actions or activities  
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What are the different types of descriptive techniques?   show
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show Find which items are closely associated in the database, including market basket analysis (MBA) and link analysis  
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What is the Clustering Descriptive Technique?   show
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Market Basket or Association Analysis can include the use of...   show
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show Generally faster to train than GRI. Allows only for specification or logical for the input variables such as (True or False) or (1,0) to indicate the presence (or absence) of the item in the market basket  
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show Means to develop a model that uses historical data to predict an outcome based on a set of input characteristics. Predictive techniques require the use of past history with the intent to predict future behavior.  
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DM techniques used to predict what will happen are classified into how many categories and what are those methods called?   show
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show Find how two or more variables are related to each other, the correlations between the variables.  
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show Curve fitting, a method used to identify a mathematical equation that can describe the relationship between the input variables and the outcomes  
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show Least square method Nonlinear Correlation Method Multivariate Correlation Techniques Inferential Statistical Techniques Statistical Techniques  
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show Artificial Neural Networks (ANN) techniques.  
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What is ANN?   show
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show They can learn  
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show DM technique that looks for the nearest neighbors of known data samples, and combines their values to assign classification or prediction values for new data samples.  
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Define Decision Tree and Rule Induction Methods:   show
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Define Rule Induction:   show
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Define CART Algorithm:   show
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show Chi-Squared Automatic Interaction Detector (C5.0), assumes one branch for each value taken on by the discrete variable. Can be used to build decision trees, but its use is restricted to discrete variables.  
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show Capacity of digital data storage worldwide has doubled every nine months for the last decade, at twice the rate  
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show Growth of computing power  
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show Known as data stores, where data are deposited to merely rest in peace just in case one day the business may be able to analyze them. Most of the time, this data is never analyzed.  
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show 1) User expectations are too high 2) Putting the right tools in the wrong hands 3) Dishing up data that users need to figure out how to use 4) Training users only at the beginning of the project  
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What are the common mistakes that organizations seeking the deployment of DM techniques must avoid?   show
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Define Real-Time Decision Support:   show
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show 1) Scaling analysis to large databases 2) Scaling to high-dimensional data and models 3) Automating the search 4) Finding patterns and models understandable and interesting to user  
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show 1) Domestic Economy 2) Internationalization 3) Government 4) Finance 5) Infrastructure 6) Management 7) Science and Technology 8) People  
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