Hypothesis Testing – Two Sample * H0 : µ1 = µ2 H1 : µ1 ≠ µ2 * Case 1 -- If you know population variances, use this and Normal table * Case 2 -- If you know only sample variances, and samples are large, use this and Normal table * Case 3 -- If you know only sample variances, and samples are small, and unknown population variances can be assumed identical, use this and t-table, with n1 + n2 - 2 df. (sp is called “pooled estimate of σ”) * We use standard error
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its popularity within Financial Intuitions, Treasures and Fund Managers, there are frequent criticisms against its use which we will discuss in this part. One of the criticisms is that VAR focuses on the risks around the middle area of the distribution and completely ignores the tail portion which is associated with large losses. (Glasserman, Heidelberger & Shahabuddin, 2002, P239). So, the probability of the portfolio losing side has not been evaluated enough. For example, the interpretation
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Independent Dependent Conditional Bayes' Rule Permutation =PERMUT(n,r) (N=count of data values) Combination =COMBIN(n,r) (R=size of smaller group taken from N) Discrete Distributions Expected Value: =SUMPRODUCT(x,P(x)) Variance: =SUM(x^2 * p) - (expected value^2) SD: =SQRT(discrete variance) Binomial Distributions Mean: =n*p Variance: =n*p*(1 - p) SD: =SQRT(variance) Probability:
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80. a. These cases are a population because that these cases account for each of the 25 tables that are filled on the night in question. b. Mean = (28 + 39 + 23 + 67 +37 + 28 +56 + 40 +28+50+ 51 + 45 + 44 + 65 + 61 + 27 + 24 + 61 + 34 + 44 + 64 + 25+ 24 + 27 + 29) / 25 = 40.84 Median = The median value is the same as the middle value, and this is the same as the 13th highest number = 39 c. The biggest value = 67, lowest value = 23; so, the range is 67 – 23 = 44 Standard deviation = sqrt([(28 - 40
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operate and maintain its power supply to provide an acceptable level of reliability to its users. This clearly requires that standards of reliability be specified and used in all three sectors of the power system, i.e., generation, transmission and distribution.
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1. Module Name: Introductory Econometrics Code: P12205 Credits: 10 Semester: Spring 2011/12 Delivery: 16 one-hour lectures + 4 one-hour workshops Aims: The main aims of this module are: to introduce students to the principles, uses and interpretation of regression analysis most commonly employed in applied economics; to provide participants with sufficient knowledge of regression methods to critically evaluate and interpret empirical research. On completion of this module students should
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Problem 1 A random sample of 16 adult males is about to get into an elevator which has a weight capacity of 2900 lbs. The adult male weight follows the normal distribution with a mean of 170 lbs and a standard deviation of 15 lbs. (a) What is the probability that the total weight for this sample of 16 adult males will exceed 2900 lbs? Hint: This is the same as calculating the probability that the average weight for a random sample of 16 exceeds 2900/16. (b) Will the probability
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ss In our report, we will base on the dividend payment history and the weekly price for each of the two stocks in the past one year, to calculate the expect return and standard deviation for each of them. According to the ANZ dividend payment history, it shows that the ANZ dividend paid semi-annually (every July and December). So it is still no dividend payment in 2013. According to the BHP dividend payment history, it shows that the BHP dividend paid semi-annually (every March and September)
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measuring the mixing properly is of great importance from the practical point of view and for the validation of theoretical models as well. In this work, the electrical resistance tomography (ERT) technique has been adopted to obtain the mixture distribution across the cross-section of the mixing vessel. The ERT has attracted many researchers' interests in the field of the process tomography because of its obvious advantages including non-intrusive measurement, high temporal resolution and non-radiological
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Quantitative Techniques in Business Introduction to Statistics In the business world, and in fact, in practically every aspect of daily living, quantitative techniques are used to assist in decision making. Why? Unlike the classroom, in the “real world” there is often not enough information available to be guaranteed of making a correct decision. For instance, if advertisers would like to know how many households in the United States with televisions are tuned to a particular television show
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