quantitative models To introduce software for solving such models. 2 Must-Read Article Inventory • • • • “Decision trees”‚ Robin Greenwood (HBR‚ March 2006) “Note on Linear Programming”‚ Jonathan Eckstein (HBR‚ November 1992) “Sampling and Statistical Inference”‚ Arthur Schleifer (HBR‚ August 1996) “Decision Analysis”‚ (HBR‚ December 1997) 3 Widely used Books for Quantitative Methods • Dance with Chance • Black Swan 4 QUANTITATIVE METHODS CASE MAPPING Chapter Basic Concepts of
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The sample forecasting project takes us through the following steps: 1. importing data into EViews from an Excel spreadsheet 2. examining the data and performing simple statistical analysis 3. using regression analysis to model and forecast a statistical relationship 4. performing specification and hypothesis testing 5. plotting results Creating a Workfile and Importing Data The first step in the project is to read the data into an EViews workfile. Before we describe the process
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RESULTS AND DISCUSSION Now that we have applied all the tools necessary for hypothesis testing‚ the final results can be discussed in detail. All variables with respect to their relation to the capital structure will be discussed separately. Not only the figures have been interpreted as per the mathematical rules‚ but they have also been analyzed according to the prevalent conditions in the cement industry during the period of analysis. Therefore‚ it is necessary to give the industry scenario
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DGHG - CCResort Business Analytics Department’s (BAD) Report Executive Summary: The Business Analytics Department has compiled this report based on data supplied by DGHG. The purpose of this report is to use this data to answer several key questions set out by DGHG‚ along with providing information as to whether the company has been meeting its key performance indicators‚ as set out in the business plan. The questions this report will primarily address‚ are as follows: * More than
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THE MONTHLY DEMAND FOR CHICKEN IN A RURAL VS AN URBAN AREA IN GUYANA A PROPOSAL INTRODUCTION The demand for chicken refers to the quantity of chicken demanded by households (in lbs) in the identified areas (one rural and one urban)‚ at the available prices within the specified areas. It must be noted at this point‚ that the true population in any given situation is never really known. As such samples are usually collected and estimated using econometric methods. The results are then used
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RESEARCH REPORT ON ROLE OF VISUAL MERCHANDISING IN FOOTWEAR IN BATA STORE FOR THE PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE AWARD OF THE DEGREE OF POST GRADUATE DIPLOMA IN RETAIL MARKETING AND MANAGEMENT Supervisor: Candidate: Dr. RITU BAJAJ Kanika Malhotra Rinky
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of the Organizational climate of Jharia division of TATA steel Ltd Contents 1. Introduction 2. About TATA STEEL Ltd: - My organisation 3. Methodology 3.1 rationale of study 3.2 Objective of the study 3.3 Hypothesis test 3.4 Questionnaire of the survey 3.5 Statistical analysis 3.6Limitation of the study 4. Survey data tabulation‚ Analysis and Interpretation 5. Findings 6. Conclusion 1. Introduction This Project is a part an assignment for the Organizational Behavior
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this research is to obtain any useful general knowledge on measures of profitability‚ customer behaviour‚ cost structure‚ profitability management and their relations with each other. The second part is an in-depth qualitative research based on statistical analyses on a database of customer profits‚ online usage‚ demographics (age‚ income‚ geographic)‚ and tenure years. Data collection for the meeting is done by simply imputing key points with brief explanations in a word document. Collecting data
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responsible for developing procedures for ensuring the similarity of batches of Guinness. The t-test was developed as a way of measuring how closely the yeast content of a particular batch of beer corresponded to the brewery’s standard. And the same statistical methodology that compares a particular batch of beer to a standard can be used to compare how different any two batches are from each other. The test can be used to compare the yeast content of two kegs of beer brewed at separate times. Extending
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Preliminary Observations • The Mean of AA Fly (7.167) is lesser than TT Air (10.229). This means that the average number of hours of AA Fly is lesser than TT Air during each 24-hour period i.e. (10.229-7.167=3.062). • The sample size of AA Fly (27) is lesser than TT Air (28). Even though AA Fly’s sample is 1 day shorter than TT Air‚ it does not significantly affect the output as the one day does not change much (unless there had been a market shock). However based on Central Limit Theorem‚
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