APPLIED PROBABILITY AND STATISTICS APPLIED PROBABILITY AND STATISTICS DEPARTMENT OF COMPUTER SCIENCE DEPARTMENT OF COMPUTER SCIENCE STATISTICAL DISTRIBUTION STATISTICAL DISTRIBUTION SUBMITTED BY – PREETISH MISHRA (11BCE0386) NUPUR KHANNA (11BCE0254) SUBMITTED BY – PREETISH MISHRA (11BCE0386) NUPUR KHANNA (11BCE0254) SUBMITTED TO – PROFESSOR SUJATHA V. SUBMITTED TO – PROFESSOR SUJATHA V
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Sample Paper Mid Term Examination All questions carry equal marks (4 Marks Each) Q.No. 1 a) i) Suppose you are an operations manager for a plant that manufactures batteries. Give an example how you could use descriptive statistics to make better managerial decisions. ii) Listed here are 30 different weekly Dow Jones industrial stock averages. 2656 2301 2975 3002 2468 2742 2830 2405 2677 2990 2200 2764 2337 2961 3010 2976 2375 2602 2670 2922 2344 2760 2555 2524 2814 2996 2437 2268 2448 2460 Construct
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Ethics and Business Statistics Integration Paper Student: Antoinette M. Ware Grand Canyon University: SYM 506 July 17‚ 2013 Abstract This paper will delineate Christian perspectives in statistics and how a personal world view is applied to the moral and ethical practice of statistics Ethical Principles for the Practice of Statistics Ethics gives the insight and morals to the statistical profession. It gives parameters for a finding a solution
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In today’s world‚ we are faced with situations everyday where Statistics can be applied. In general‚ Statistics is the science of collecting‚ organizing‚ and analyzing numerical data. The techniques involved in Statistics are important for the work of many professions‚ thus the proper preparation and theoretical background of Statistics is valuable for many successful career paths. Marketing campaigns‚ the realm of gambling‚ professional sports‚ the world of business and economics‚ the political
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as special cases of the F distribution: (3) Example: We want to measure the monthly sales volume from Microsoft and Apple. We collect data for a year ( 12 months). We calculate the variance for both and define the “degrees of freedom’ (n-1= 11) and then we can build the F-distribution. F statistic (): Defined as the ratio of the dispersions of the two distributions‚ in other words it is the value calculated by the ratio of two sample variances . F always >=1. The F statistic can test the
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Case Study 1: Par‚ Inc. Par‚ Inc.‚ is a major manufacturer of golf equipments. Management believes that Par’s market share could be increased with the introduction of a cut-resistant‚ longer-lasting golf ball. Therefore‚ the research group at Par has been investigating a new golf ball coating designed to resist cuts and provide a more durable golf. The tests with the coating have been promising. One of the researchers voiced concern about the effect of the new coating on driving distances. Par
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TOPIC 1 INTRODUCTION & DESCRIPTIVE STATISTICS BASIC CONCEPTS Situation: A journalist is preparing a program segment on what appears to be the relatively disadvantaged financial position of women and the incidence of female poverty in Australia. Several questions may arise‚ for example: • What is the pattern of female incomes? • How severe is the problem of female poverty and what proportion fall below the ‘poverty line’? • Has their general level of income improved over
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Statistics for Business and Economics Personal pre-assignment 1.9 What is a representative sample? What is its value? The representative sample is a subset of a population of interest that is exhibiting the typical characteristics of the population. The most typical way to cover the up-mentioned criterion is the simple random sample which consist of a sample of units that is selected randomly‚ e. g. the sample is selected form the population on a way that every sample of the size is having
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What is Bootstrapping (In Statistics)? Bootstrapping is an interesting process or technique of assigning measure of accuracy. Depended upon calculation‚ Bootstrapping can be used to any statistic to measure estimation. Definition According to the Cambridge dictionary of statistics – “A confidence interval is a range of values‚ calculated from the sample observations that are believed‚ with a particular probability‚ to contain the true parameter value. A 95% confidence interval‚ for example‚ implies
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CASE 1 - DEMAND ESTIMATION and ELASTICITY: Soft Drinks in the U.S. Demand can be estimated with experimental data‚ time-series data‚ or cross-section data. In this case‚ cross-section data appear in the Excel file. Soft drink consumption in cans per capita per year is related to six-pack price‚ income per capita‚ and mean temperature across the 48 contiguous states in the United States. QUESTIONS 1. Given the data‚ please construct (a) a multiple linear regression equation and (b) a log-linear
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