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    Problem Set 3

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    Problem Set 3 1. Louise McIntyre’s monthly gross income is $2‚000. Her employer withholds $400 in federal‚ state‚ and local income taxes and $160 in Social Security taxes per month. Louise contributes $80 per month for her IRA. Her monthly credit payments for VISA‚ MasterCard‚ and Discover card are $35‚ $30‚ and $20‚ respectively. Her monthly payment on an automobile loan is $285. What is Louise’s debt payments-to-income ratio? Is Louise living within her means? Louise’s Gross Income | =

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    Problem Set 1

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    Problem Set 1 Macroeconomics‚ ECON 2123 (Instructor: Partha Sen; TA: Peter Tsui) ------------------------------------------------------------------------------------------------------------------------------------ Posted: Friday‚ 19 September 2014 Due: 5:00 PM Friday‚ 26 September 2014 (Submit your homework into the homework collection box outside the department office) 100 marks total Part I: True/False/Uncertain (20 marks‚ 4 marks each) 1. GDP is the value of all goods and services produced

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    Problem Sets: Economics

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    One: (10 points) Tom leases a farmer’s field and grows pineapples. Tom hires students to pick and pack the pineapples. The following table sets out Tom’s total product schedule. Labor (students) Total Products (pineapples/per day) 0 0 1 100 2 220 3 300 4 360 5 400 6 420 7 430 1. Calculate the marginal product of the third student; 2. Calculate the average product of three students; 3. Over what numbers of students does marginal product increase? 4. When marginal

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    Distinguish between primary data and secondary data? Primary data is the data that is collected first hand from the original source for the purpose of making statistical inference while secondary data is the data that is collected by the method of abstraction and is used to make statistical inference by using primary data already collected by an investigator. Primary data is collected by  Identifying population of interest  choosing sample  analyse sample information  draw inference from

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    Data Science and Prediction

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    http://hdl.handle.net/2451/31553 Data Science and Prediction Vasant Dhar Professor‚ Stern School of Business Director‚ Center for Digital Economy Research March 29‚ 2012 Abstract The use of the term “Data Science” is becoming increasingly common along with “Big Data.” What does Data Science mean? Is there something unique about it? What skills should a “data scientist” possess to be productive in the emerging digital age characterized by a deluge of data? What are the implications for business

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    1. With necessary diagram explain about data warehouse development life cycle . Ans : Introduction to data warehouses. Data warehouse development lifecycle (Kimball’s approach) Q. 2. What is Metadata ? What is it’s uses in Data warehousing Archietechture ? Ans : In simple terms‚ meta data is information about data and is critical for not only the business user but also data warehouse administrators and developers. Without meta data‚ business users will be like tourists left

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    Text Problem Sets

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    A-3 (Coverage ratio) The firm in the two preceding problems also had $6 million of principal repayments during the latest 12 months. Its marginal tax rate is 40%. Calculate the debt service coverage ratio. Debt-Service Coverage Ratio = (EBIT + 1/3 Rentals) / (Interest Expense + 1/3 Rentals + Principal Repayments / (1 - T)) = ($30 + $15 / 3) / ($10 + $15 / 3 + $6 / (1 - 0.40)) = 1.40 A-4 (WACC with rebalancing) Nathan’s Catering is a gourmet catering service located in Southampton‚ New York

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    data gathering

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    Library Observation Reflection This activity was one that I thought would be interesting but‚ to be honest‚ it wasn’t one in which I entered with a lot of expectations. I wasn’t really sure what would happen so I tried to approach the exercise with an open mind. My perceptions of the library did change from this exercise. Prior to this observation I have always just kind of used the library. I have used it a lot‚ but generally I will do all of my searches online. Thus‚ when using the library

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    the Study 6. Motivation REVIEW OF RELATED LITERATURE AND STUDIES 1. Review of Related Literature 2. Review of Related Studies 3. Conceptual Framework 4. Operational Definition of Terms METHODOLOGY 1. Methods of Research 2. Data Gathering Techniques 3. Statistical Treatment of Data (optional) SYSTEM PRESENTATION A. Existing System 1. Company Background 2. Description of the System 3. Process Flow of the System 4. Analysis of the System B. Proposed System 1. Description of the System 2. Objectives of

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    apply. The inputs for the multiple are publicly available‚ though are vulnerable to accounting manipulation. Also‚ it is difficult to obtain a truly comparable large sample of firms. Multiples analysis is backward-looking‚ reliant on historical/current data to obtain multiples. It reflects relative value rather than the intrinsic value which DCF valuation produces.

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