Randomized Control Trial Free Essay Example, 2000 words

Randomized Control Trial (RCT) is recognized widely as the best way of making a comparison between the different modes of treatment and how effective they are. Therefore, I am not of the opinion that the results of the Random Control Trial conducted by Ebenbichler et al are the only method of deriving information about the particular intervention. Several trials and research have found that there are other viable methods of managing Calcific Tendinitis that can lead to healing (Maxwell 1992, p. 421). Additionally, other research methods might have glaring differences that suggest different hypotheses and therefore without the application of the meticulous criteria set by RCT, the degree of certainty of an outcome can be limited. The results are influenced by either an over or underestimated the relationship between the intervention and the condition being studied. It is important to note that Randomized Control Trials may at times be biased if poorly performed, and have deficiencies in the report it gives. The discrepancy that may be found in different aspects of the study that should be reported and those that are reported should be known (Begg et al 1996, p. 637) thereby requiring readers to make a judgment based on what has already been reported and are in the domain of the public. We will write a custom essay sample on Randomized Control Trial or any topic specifically for you Only $17.96 $11.86/pageorder now I think one obvious inadequacy of the research carried out by Ebenbichler et al (1999) is that they fail to specify the type of randomization they adopted for their study and how they have narrowed down to the seventy trialists. The researchers state that they enrolled seventy patients of whom sixty-three participated in the study. There were thirty-two shoulders that participated in the genuine ultrasound treatment and twenty-nine in the fake ultrasound treatment out of the sixty-one that completed the study. The simple randomization that Ebenbichler et al have adopted is less reliable as compared to the stratified randomization that looks appropriate in such circumstances where there may be variables. This method follows the fundamental principle of randomization in which every individual has the same chance of being allocated into the groups.

ESL Curriculum Planning for Teaching English

This curriculum plan for non-trained teachers of ESL/EFL focuses on building a program for your class or private students. The first part focuses on the basics of ESL. There are a few important aspects to always keep in mind while developing any curriculum, be it only a few lessons or a full course: Language skills need to be recycled many times before they are actively acquired.All language skills (reading, writing, speaking, and listening) should be involved in the learning process.Understanding grammar rules does not necessarily mean that a student can use that grammar, as students need to actively practice skills they are learning. Language Recycling An acquired language needs to be repeated in a various number of guises before it can be actively used by the student. Studies have shown that new linguistic functions need to be repeated at least six times before most learners can consider the new piece of language theirs. After six repetitions, the newly-acquired language skills are usually still only passively activated. The learner will require more repetitions before he or she will be able to use the skills actively in everyday conversation. Here is an example of language recycling using the present simple: Work on the present simple rules.Read an article about the daily routines of someone.Listen to someone who describes his or her daily tasks.Have a discussion asking him or her to describe what he or she does on a daily basis. Use All Four Skills Employing all four linguistic skills (reading, writing, listening, and speaking) when working through a lesson will help you recycle language during the lesson. Learning rules are important, but, in my opinion, practicing the language is even more important. Bringing all these aspects into a lesson will add variety to the lesson and help the learner pragmatically practice the language. Ive met many learners who can knock off a grammar sheet without a mistake and then  when asked, Could you describe your sister? they have problems. This is generally due to the emphasis in many school systems for learning grammar. Putting It All Together So, now you understand the basic tenets of teaching English effectively. You might be asking yourself the question what do I teach? When planning a course, most coursebooks build their curriculum around certain themes that help glue everything together. While this can be rather complicated, I would like to provide a simple example that develops the present simple and past simple. Use this type of outline to build your lesson and remember to provide a number of elements, including listening, reading, writing, and speaking. You will find that your lessons have a  purpose and specific objectives which are clearly definable, like helping you and your learners recognize the progress you are making. Who are you? What do you do? (Daily routines)A present simple example: What do you do? I work at Smiths. I get up at seven, etc.To be present example: Im married. Shes thirty-four.Descriptive adjectives example: I am tall. He is short.Tell me about your past. Where did you go on your last holiday?A past simple example: Where did you go on holiday when you were a child?To be past example: The weather was fantastic.Irregular verbs example: Go — went; Shine — shone Finally, the lesson generally will be divided into three principal sections. Introduction:  Introducing or reviewing grammar or function.Development: Taking that grammar and working on it in reading, listening, and other forms. This section should make up the bulk of your lesson and include a number of different activities, if possible.​Review: Review the principle concepts covered during the lesson. This can be very straightforward and either student or teacher-led, depending on the level of your learners.

What You Dont Know About The Benefits of Essay Samples Could Be Costing to More Than You Think

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Implementation of Linear Programming Problem Optimization of Bicycle

Questions : 1. The general manager of bicycle manufacturing company would like to find out how many bicycles and scooters that the production department can build next month. The decision making is based on the following information below. The production of each bicycle consumes 20 days of labour and $1000 of materials. The production of each scooter consumes 50 days of labour and $1500 of materials. Each bicycle brings $800 profit to the company, while each scooter brings $1200 profit to the company. The company has a capacity of 3000 working days per month, and the budget for the materials is $250000 per month. The sales department requires that at least 50 units of bicycles and 20 units of scooters be produced per month. 2. Explain the main difference between big data and big data analytics. Give a real life example on how big data can be useful for supporting business operations.3. What is the importance of a collaborative technology? Provide the name of a software that is used for collaboration and discuss how this technology is used. Answers : 1. Since, the general manager of bicycle manufacturing company would like to find out how many bicycles and scooters that the production department can build next month to attain maximum profit; here the decision variables are number of bicycle (X1) and number of scooters (X2). As the profit from one unit of bicycle is $800 and profit from one unit of scooter is $1200, here the objective function will be Max Z = 800 *X1 + 1200 * X2 There were labour constraint, material constraint and production unit constraints as follows: Labour constraint: 20 * X1 + 50 * X2 3000 Material constraint: 1000 * X1 + 1500 * X2 250000 Production constraints: X1 50 X2 20 Non negativity constraints; X1, X2 0 Thus the LP problem looks like: Max Z = 800 *X1 + 1200 * X2 Subject to, 20 * X1 + 50 * X2 3000 1000 * X1 + 1500 * X2 250000 X1 50 X2 20 And X1, X2 0 Using Microsoft Excel solver, the LP is solved. The details of the solution are given as below: Mathematical model for solving LP: Variables X1 X2 Coefficients 800 1200 Solutions 100 20 Z 104000 Cons1 20 50 3000 Cons2 1000 1500 250000 Cons3 1 50 Cons4 1 20 LHS RHS Cons1 3000 3000 Cons2 130000 250000 Cons3 100 50 Cons4 20 20 Excel output: Microsoft Excel 12.0 Answer Report Worksheet: [KHA 49323.xlsx]Sheet1 Report Created: 1/20/2015 2:55:47 PM Target Cell (Max) Cell Name Original Value Final Value $E$7 Z X1 0 104000 Adjustable Cells Cell Name Original Value Final Value $E$6 Solutions X1 0 100 $F$6 Solutions X2 0 20 Constraints Cell Name Cell Value Formula Status Slack $E$17 Cons1 LHS 3000 $E$17=$F$17 Binding 0 $E$18 Cons2 LHS 130000 $E$18=$F$18 Not Binding 120000 $E$19 Cons3 LHS 100 $E$19=$F$19 Not Binding 50 $E$20 Cons4 LHS 20 $E$20=$F$20 Binding 0 From this above solution, it can be said that maximum number of bicycle is 100 units and maximum number of scooters is 20 units. Here, the profit for the next month will be $104000. 2. Difference between Big Data and Big Data Analytics Big data is different from big data analytics. The three major aspects of difference are discussed below: Volume: The analysts firm, International Data Corp (IDC), has undertaken a research and indicated that the amount of digital data is growing across the globe and it is expected to be 40,000 Exabyte by 2020. On the other hand, big data analytics is the procedure of scrutinizing huge data set containing different type of data or big data. Hence, the volume of big data analytics is not growing in the same manner as big data. Big data analytics is consisted of some tools which are important for extracting the relevant information (Sathi, 2012). Velocity: The speed of data is considered to be more important than its volume. It is impossible to deal with the big data in a specific period of time. It will be very difficult and a slow activity to access the big data for specific purpose. The raw data is not useful to the consumers. On the other hand big data analytics have made enables the organization to access real time and non-real time information for making decision in a quicker method. It also helps in implementation of the moves in a faster way in comparison to its competitors (Baesens, 2014). Variety: It has been found that big data comes in different forms. For example, if someone posts a photograph on face book or sends via e-mail, tweeter or using other social media networks, it will generate data. Different forms of data are available in an unstructured manner. On the other hand, data analytics is analyzing the data and uses some specific elements of computing such as memory, storage, bandwidth etc (Baesens, 2014). Real example of application of big data The researchers of MIT Media Lab had used the location data from the cell phones and they had successfully determined number of people in the parking lot of Macy on Black Friday. Additionally, the initiation of Christmas shopping in US was forecasted by using big data (Arellano, 2015). 3. In the present situation the companies requires the workers to perform faster and more productively and achievement of that goal is possible with the introduction of the collaborative technologies in addressing the collaborative behaviors of the employees. The use of the collaborative technologies not only gives the employees an opportunity to discuss their work but also creates new ways for employees to deliver a particular job. The following are some of the advantages of implementing collaborative technology (Ding, Yu and Sun, 2012) . Initiates new process of performing jobs The organizations by embedding the collaborative technologies into the daily work schedule can effectively make it a natural and acceptable part of the job. For instance by sharing ideas via online platforms like Face book, Twitter and Myblogs, employees at CEMEX were able to reduce the CO2 measures by 1.8 million metric tons per year. Similarly the engineers at GE aviations were able to solve problems in short span of time by sharing documents (Ding, Yu and Sun, 2012). Guidance about process performance Ding, Yu and Sun (2012) opined that the collaborative technologies can also provide guidance about the best ways in which a particular process may be performed in order to increase the quality and th productivity of the process. For instance the Social workflow platforms can provide standardized group work plans with detailed tasks, and instructions. The group members gets the opportunity to use this platform as a source of sharing updates, submit reports, review check lists and approvals.Tan, (2012) opined that the sharing of the ideas and documents helps the group members to ask for any kind of support or help. Shaping of the collaborative behavior The collaborative technologies aim in shaping, encouraging and motivating of desired collaborative behaviors within the organization. The organizations in this respect have introduced various incentives like collaborative participation game and social network using reward. The companies in order to encourage the act of collaboration introduced rewards and corporate recognitions for the employees Employee talent management An effective collaboration technology supports the present work capabilities of the employees as well as creates opportunities for future working opportunities (Tan, 2012). Majority of the companies are adopting new operating models in which the organization will have to deal with vendors, outsourcers, partners, suppliers and customers hence implementing a collaborative technology will help in organizing all the related stakeholders. Timely completion of projects The projects are completed before the schedule because of the use of project collaborative management software. The organizations are able to collaboratively employ all the necessary groups and team members with the help of collaborative software like Wrike and on stream meetings. This software enables around 100 participants to enter into virtual digital meetings at the same time (Ding, Yu and Sun, 2012). The face to face interaction of the team members helps in ascertaining the loopholes of the project and also helps to judge the feasibility of the project. Hence the companies suffer no financial loss due to delay of projects. The opinions of the different team embers of different sectors are also prioritized (Tan, 2012). Some of the major and most popular collaborative software used in small as well as large organizations are namely LiquidPlanner, Wrike, Confluence, On stream meetings, XaitPorter,Webex meeting centre, Proof hub etc. Among them Wrike is a collaborative software that provides a virtual real time work space where different teams of an organization can collaborate in order to complete a project successfully with quality. The organizations feel it as user friendly software because the collaboration is easy and the group members of different groups are able to get a clear view of the progress of the organization. Moreover the process of open communication between the group members helps the organization to manage the project completion schedule and also the resources effectively. The real time decision making increases the pace of the project. Organizations like PayPal, Google, Hootsuite, Ecco, Adobe, HTC and EMC prefer using this software for the purpose of critical project completion. Tan (2012) opined that the collaboration of the teams like the sales team, marketing, communication, product and Human resource make the decisions faster for the organization and also helps in completion of the project without any flaw. The avoidance in the delay of the project schedule also makes the project financially feasible for the organizations. References Arellano, N. (2015).3 key differences between big data and analytics. [online] IT World Canada. Baesens, B. (2014).Analytics in a big data world. Hoboken, N.J.: Wiley. Ding, B., Yu, X. and Sun, L. (2012). A Cloud-Based Collaborative Manufacturing Resource Sharing Services.Information Technology J., 11(9), pp.1258-1264. Sathi, A. (2012).Big data analytics. Boise: MC Press. Tan, X. (2012). A Contextual Item-Based Collaborative Filtering Technology.Intelligent Information Management, 04(03), pp.85-88.