Bivariate analysis Contingency table In this case‚ we use contingency table to analyze the relationship between 2 qualitative variables. And this test works by comparing expected and observed frequencies with x2 distribution. Correlation coefficient When we need to test the relationship between 2 quantitative variables‚ we use correlation coefficient and it measured by standardized covariance measure and investigates linear dependence. Before doing this‚ it is better to first make a scatterplot
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A statistical theory that states that given a sufficiently large sample size from a population with a finite level of variance‚ the mean of all samples from the same population will be approximately equal to the mean of the population. Furthermore‚ all of the samples will follow an approximate normal distribution pattern‚ with all variances being approximately equal to the variance of the population divided by each sample’s size. Using the central limit theorem allows you to find probabilities for
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Error 0.022666 Median 0.0607 Median 0.0511 Median 0.0108 Mode -0.5085 Mode -0.8652 Mode -0.3641 Standard Deviation 0.305747 Standard Deviation 0.489717 Standard Deviation 0.22552 Sample Variance 0.093481 Sample Variance 0.239822 Sample Variance 0.050859
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8%)] = – 4.4% 18% – 8% = 10% αC = 17% – [8% + 0.7 × (16% – 8%)] = 3.4% 17% – 8% = 9% αD = 12% – [8% + 1.0 × (16% – 8%)] = – 4.0% 12% – 8% = 4% Stocks A and C have positive alphas‚ whereas stocks B and D have negative alphas. The residual variances are: 2(eA ) = 582 = 3‚364 2(eB) = 712 = 5‚041 2(eC) = 602 = 3‚600 2(eD) = 552 = 3‚025 b. To construct the optimal risky portfolio‚ we first determine the optimal active portfolio. Using the Treynor-Black technique‚ we construct the active
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Remington’s Steakhouse Project Brian Jones Research Methods & Applications Dr. Jones August 25‚ 2011 Table of Contents Table of Contents 2 List of Tables 3 Introduction 4 The Research Objectives 4 The Research Questions 5 Literature Review 6 Answers to Research Questions 8 Recommendations to Remington’s 15 References 18 Annotated Bibliography 19 Appendix(ces) 22 List of Tables Table 1 Demographic Description of the Average Remington’s Patron9 Table 2 Reported Income by Remington’s
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Skewness‚ Kurtosis‚ and the Normal Curve Skewness In everyday language‚ the terms “skewed” and “askew” are used to refer to something that is out of line or distorted on one side. When referring to the shape of frequency or probability distributions‚ “skewness” refers to asymmetry of the distribution. A distribution with an asymmetric tail extending out to the right is referred to as “positively skewed” or “skewed to the right‚” while a distribution with an asymmetric tail extending out to
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American Finance Association Efficient Capital Markets: II Author(s): Eugene F. Fama Source: The Journal of Finance‚ Vol. 46‚ No. 5 (Dec.‚ 1991)‚ pp. 1575-1617 Published by: Blackwell Publishing for the American Finance Association Stable URL: http://www.jstor.org/stable/2328565 Accessed: 30/03/2010 21:19 Your use of the JSTOR archive indicates your acceptance of JSTOR ’s Terms and Conditions of Use‚ available at http://www.jstor.org/page/info/about/policies/terms.jsp. JSTOR ’s Terms and
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required Beta Distribution • used to describe probabilistic time estimates. In special interest in network analysis is the average or expected time for each activity and the variance of each activity time. The expected time of analysis is a weighted average of the three estimates: te- expexted time ∂2 - variance of each activity time ta + 4tm +tp te= ----------------------- 6 The expected duration of a path is equal to the sum of the expected times of the
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weights to the observations we can set s i 1 i u m 2 n 2 n i where m i 1 i 1 5 ARCH(m) Model AutoRegressive Conditional Heteroskedasticity In an ARCH(m) model we also assign some weight to the long-run variance rate‚ VL: s VL i 1 i u m 2 n 2 n i where m i 1 i 1 6 ARCH(m) Model AutoRegressive Conditional Heteroskedasticity Robert Fry Engle is an American economist and the winner of the 2003 Nobel Memorial
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2.0782 g For sample C: % of ash = 2.5527 g-2.4859 g X 100 = 2.5709% 2.5527 g For average: % of ash = 2.2956 g-2.2365 g X 100 = 2.5745% 2.2956 g Variance of Ash: s2 = ∑ x2 – (∑x)2 n n-1 = 15.1142 g – (45.0174 g/3) = 0.0542 2 Standard Deviation of Ash: s= √s2 =√0.0542 =0.2328 DISCUSSION: Ash is the inorganic residue
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