National Cranberry Cooperative 2. The resource with least capacity determines the maximum long-term achievable throughput rate. Because wet and dry berries follow different routes at RP#1 there will be a maximum achievable throughput for each. The capacity of the dryers is the bottleneck for the wet berries. The maximum throughput for wet berries is 600 bbls/hr. For dry berries the separation process is the bottleneck. The maximum throughput for dry berries is 1200 bbls/hr. The percentage
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study: MBA Course Title: Marketing Research Course code: MBA 763 Assignment: Secondary Data Mat Number: 74168 Name: Abiona Timothy Olufemi What is Data Data is a collection of facts‚ such as numbers‚ words‚ measurements‚ observations or even just descriptions of things. 1.Information in raw or unorganized form (such as alphabets‚ numbers‚ or symbols) that refer to‚ or represent‚ conditions‚ ideas‚ or objects. Data is limitless and present everywhere in the universe. See also information and knowledge
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DATA INTEGRATION Data integration involves combining data residing in different sources and providing users with a unified view of these data. This process becomes significant in a variety of situations‚ which include both commercial (when two similar companies need to merge their databases and scientific (combining research results from different bioinformatics repositories‚ for example) domains. Data integration appears with increasing frequency as the volume and the need to share existing data explodes
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using the Adjusted Present Value (APV) approach assuming the firm raises $750 thousand of debt to fund the project and keeps the level of debt constant in perpetuity. NPV of Levered Firm = $1‚528‚485 3. Value the project using the Weighted Average Cost of Capital (WACC) approach assuming the firm maintains a constant 25% debt-to-market value ratio in perpetuity. NPV of Levered Firm = $1‚469‚972 4. How do the values from the APV and WACC approaches compare? How do
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IT433 Data Warehousing and Data Mining — Data Preprocessing — 1 Data Preprocessing • Why preprocess the data? • Descriptive data summarization • Data cleaning • Data integration and transformation • Data reduction • Discretization and concept hierarchy generation • Summary 2 Why Data Preprocessing? • Data in the real world is dirty – incomplete: lacking attribute values‚ lacking certain attributes of interest‚ or containing only aggregate data • e.g.‚ occupation=“ ”
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Data Processing All through the different stages in civilization‚ man has always tried to look for ways to simplify work and to solve problems more efficiently. Many problems involved numbers and quantities‚ so man started looking for easier ways to count‚ to add‚ subtract‚ multiply and divide. As society has grown in both size and complexity‚ so have data that are generated by it through time. Definition of Terms Data – is defined as any collection of facts. Thus sales reports‚ inventory
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Butler Lumber Company 1. Why does Mr. Butler have to borrow so much money to support this profitable business? 2. Do you agree with his estimate of the company’s loan requirements? How much will he need to borrow to finance his expected expansion in sales (assume a 1991 sales volume of $3.6 million) 3. As Mr. Butler’s financial adviser‚ would you urge him to go ahead with‚ or to reconsider‚ his anticipated expansion and his plans for additional debt financing? As the banker‚ would you
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Data Preprocessing 3 Today’s real-world databases are highly susceptible to noisy‚ missing‚ and inconsistent data due to their typically huge size (often several gigabytes or more) and their likely origin from multiple‚ heterogenous sources. Low-quality data will lead to low-quality mining results. “How can the data be preprocessed in order to help improve the quality of the data and‚ consequently‚ of the mining results? How can the data be preprocessed so as to improve the efficiency and ease
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CHAPTER 2 DATA COLLECTION AND PRESENTATION 2.1 Data Collection This section aims to: 1. Identify‚ compare and contrast the different types of data; 2. List and explain the various techniques of selecting a sample; and 3. Enumerate and illustrate the different sampling techniques Types of Data Data is a collection of facts‚ such as values or measurements. It can be numbers‚ words‚ measurements‚ observations or even just descriptions of things. Two types of Data Primary Data means original
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TYPES OF DATA AND COMPONENTS OF DATA STRUCTURES Data types 1. Primitive: is a data type provided by a programming language as a basic building block 2. Composite: is any data type which can be constructed in a program using its programming language’s primitive data types and other composite types 3. Abstract: is a mathematical model for a certain class of data structures that have similar behavior; or for certain data types of one or more programming languages that have similar semantics
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