IEE 572 Term Project EXECUTIVE SUMMARY The purpose of the experiment is to analyze factors that affect the texture of the Rice. We chose to conduct the experiment with 4 factors which were thought to be the most important ones. Each factor is run at two levels so that the experiment is a 24 factorial design with two replicates. The experiment runs were randomized and the results were analyzed using Design Expert Software. This software helped us in identifying
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482 IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART B: CYBERNETICS, VOL. 42, NO. 2, APRIL 2012 An Adaptive Differential Evolution Algorithm With Novel Mutation and Crossover Strategies for Global Numerical Optimization Sk. Minhazul Islam, Swagatam Das, Member, IEEE, Saurav Ghosh, Subhrajit Roy, and Ponnuthurai Nagaratnam Suganthan, Senior Member, IEEE Abstract—Differential evolution (DE) is one of the most powerful stochastic real parameter optimizers of current interest. In this
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1. Suppose we are interested to estimate the proportion (p) of smokers among the male students in IIMA. Suppose a sample of size 100 is chosen out of the male students and the sample proportion of smokers is found to be 0.2. Give an interval based on your data so that you are 95% confident that the true value of the unknown proportion lies inside it. How would you explain 95% confidence to a layman? Suppose a professor of IIMA thinks that true proportion is 0.3. Are you ready to accept the professor’s
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Distribution of Wealth? Wealth has affected american society and evermore has affect the fabric of families. Has the distribution of wealth gotten so out of control that the effect will reverberate for generations to come and as a society can we do something about it. When we were tasked to write this final research paper on one of the topics from the discussions over the past semester i knew that i had to do a topic that i had dealt with on some personal level even know i have experienced many
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PHILIPPINE WOMEN’S UNIVERSITY Waiting Line Theory A Narrative Report 2014 MBA PROGRAM Introduction Each organization has different illustrations of waiting lines or commonly called “queue”. No wonder why we also need to study waiting line because it is part of our everyday routine as well. It merely affects the performance and profit of the company. Waiting line or queue refers to a busy service facility or server so service is momentarily occupied or being used. One great example
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Bottling Company Case Study | MAT 300: StatisticsProfessor: Dr. Negash Begashaw September 2, 2014 | Maggie L. Moore | Customers have begun to complain that the bottles of Lonice’s Soda contain less than the advertised sixteen ounces of soda. Today I asked my employees to randomly pull thirty bottles off the lines at random from all the shifts at the Lonice’s bottling plant. After asking them to measure the amount of soda in each bottle we came up with the following data. (See attached spread sheet)
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Lucy | 0/10 | 2/10 | 0/10 | Tiffany | 2/10 | 0/10 | 3/10 | Skye | 3/10 | 0/10 | 0/10 | Seanie | 0/10 | 1/10 | 1/10 | Amanda | 2/10 | 2/10 | 0/10 | After, we got all of the data we had to determined if each test was a Normal Distribution in order to make a graph. At first we noticed that test 2 & 3 were weird because their median and Q1 in test 2 were the same. When we made box and whiskers graphs and only test 1 made sense. This is how we determined that: After
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Ballard Integrated: Human Resource Data Team “B” has been assigned to assist Ballard Integrated Managed Services Inc., (BMIS) determine why turnover rates have increased, and morale has decreased at the Douglas Medical Center site. BMIS is a national company that handles housekeeping, food service, general cleaning, and plant maintenance for more than 153 companies, including 22 Fortune 100 businesses, 16 major universities, 14 medical centers, 3 large regional airports, and more than 100 midsize
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)/10 . 10 Some experimental measurements have also suggested Gaussian to be a good enough yet simple fit for the distribution of ω(q) [17]. We will take advantage of this Gaussian simplification later in our framework. As for the shadowing variable, log-normal is shown to be a good match for the distribution of ΥSH (q). Then, we have the following zero-mean Gaussian pdf 2 1 for the distribution of ν(q): fν (x) = √2πα e−x /2α , where α is the variance of the shadowing variations around path loss. Characterizing
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Life Changes Scaling For The 1990s Introduction * In this experiment, the purpose of it is to reduce all the error samples that were made in the original experiment created by Rahe and Holmes * The original research happened 3 decades ago * Social Readjustment Rating Scale is the creation of the original scaling study compromised with the Schedule of Recent Experience from the first questionnaire that collected the recent changes a person went through information Method *
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