February 27, 2009
Mr. Rodge Smith Manager, Galvasteel Corporation 324-122 Kiloahwa, Hawaii
Dear Mr. Smith: After reading your letter of February 18,2009, I can thoroughly understand why you are running out of patience. While it would be easy to place the blame on our computer, this poor fellow has received enough abuse since joining our firm. After all, he only follows the orders that are given to him. Therefore, please accept my apology for the delay in refunding your money.
Our bookkeeping department has been instructed to issue a check to you at once, which you should be receiving within a few days. I am grateful that your letter was brought to my attention and I appreciate your perseverance in settling this matter. Once again, I am very sorry for the inconvenience this has caused you.
Sincerely Yours, Meilyn Baysa
February 27,2009 Mrs. Nenita Slogan Health Economics Instructor Lawiswis State University
Dear Mrs. Slogan: This is to acknowledge receipt of your letter of January 30, 2008 in which you requested a three month extension on your insurance loan, number 3528 After careful review, we regret to inform you that we are unable to grant you any further extensions for the payment on your loan. We are sorry about the difficulties you are experiencing, but we must insist on receiving your payment by February 10, 2009. We hope that you will be able to find another solution to your problem.
Very Truly Yours, Meilyn Baysa
Date: February 27, 2009
To: Ms. Sanelie Corpuz Brgy. #12 San Nicolas Ilocos Norte, 2908
We appreciate your interest in being employed by our firm. We regret to inform you, however, that the available position(s) has been filled, and we cannot give your application further consideration at the present time. Your application will be kept on file for future reference should an opening arise.
Very truly,
Meilyn Baysa Manager, Diamond Peel Spa Makati Branch, (0910-7878345)
February 27, 2009
Miss Lea Limin Sha-Mae Appliances Laoag City
Dear Miss Limin: Pursuant to your request, we have changed your meeting with Mr. Ricky Sandoval; Accounting Manager to 9:00AM, on March 3, 2009.
We are pleased to be able to accommodate you in this manner, Mr. Ricky Sandoval will be looking forward to your meeting on this newly appointed date.
Very Truly Yours,
Meilyn Baysa Assistant Secretary Sha-Mae Appliances
Northwestern University College of Allied Sciences School of Midwifery
February 27, 2009
Dear Mrs. Rios,
I am submitting to your good office the different styles of Business Letters namely: 1. Apology for delay of refund 2. Letter of Denial of Extension of time 3. Acknowledgement of Application 4. Acknowledgement of Change in Meeting Date
Very Truly Yours, Meilyn Baysa
ARGUMENTATIVE DISCOURSE
To communicate in the forms of discourse appropriate to one or more fields of study.
1 . To identify the forms of discourse appropriate to given fields of study 1.1 Accurate recognition of specialized vocabulary and conventions. 1.2 Accurate recognition of the characteristics of the form of discourse. 2 . To recognize the discursive frameworks appropriate to given fields of study 2.1 Clear and accurate recognition of the main ideas and structure. 2.2 Appropriate distinction between fact and argument. 3 . To formulate a discourse 3.1 Appropriate choice of tone and diction. 3.2 Correctly developed sentences. 3.3 Clearly and coherently developed paragraphs. 3.4 Appropriate use of program-related communication strategies. 3.5 Formulation of a 1000-word discourse. 3.6 Thorough revision of form and content.
NARRATIVE DISCOURSE
Definition A narrative discourse is a discourse that is an account of events, usually in the past, that employs verbs of speech, motion, and action to describe a series of events that are contingent one on another, and that typically focuses on one or more performers of actions. Features
Events are organized chronologically. First or third person pronoun forms are used. The text is oriented around a specific agent or agents.
Examples
Folk stories
o
Stories about real or imagined ancestors, often containing supernatural elements
Historical events
o
Stories or accounts about the social and political history of the world and its contacts with the rest of the world
Mythology
o
Stories explaining origins, natural phenomena, or social and religious customs, often involving the supernatural
Personal experience
DESCRIPTIVE DISCOURSE It describes a person, place, things, and idea for a reader. It includes feelings of a person, place, or things that is being describe
ELEMENTS
Descriptive details Like the shape, size and colors
Precise Words Exact words to draw a word picture Example: COLOR - general RED – precise
Grammar Link Include variety of sentences, adjectives, and adverbs ( to show comparison)
Sensory Details Use of words that appeal to 5 senses so that the details will appear more alive
Figurative Language Describe something by comparing it to another thing that is very different
Literal Language Describe exactly how something looks like, tastes like, feels like, smells like, or sounds like. Example: Literal – She was upset Figurative – She cries like a baby
In writing the first draft or topic sentence, you should consider the following:
Introduction Body Conclusion
TECHNICAL REPORTS 1. Introduction
These reports were were written as part of a larger project, directed by Prof. Cliff Konold, University of Massachusetts at Amherst, on "A study of student investigations in data-sharing projects" (NSF Grant No. REC-9725228). For details about the background of this project and the intentions of these technical reports see background of the project (written by Cliff Konold).
2. Principles of the technical reports on the 5 data sharing projects
The technical reports have practically all the same structure, which we chose according to our intentions:
A. Summary B. Introduction C. Data and data archives D. Tools for data analysis E. Data analysis in the curriculum
The Introduction provides a brief summary of the project so that our analyses that will follow can be understood and so that the reader gets an idea of the project. We use as many texts from the project's website as we can and show these quotations by using smaller fonts. We provide links to the original project site. In Data and data archives we describe in detail which data (variables, measurement procedures, metadata), where on the web and in which format they are available. Projects differ very much in how well the data are structured and how well the definition of variables is documented. Sometimes a specific interface supports the selection of data from a large archive. We regard a data archive separated from the rest of the material but linked to it as advantegeous. The paragraphs on Tools for data analysis analyzes which tools are provided by the projects to analyze their own data and for which data analytical purposes they are adequate. Relatively simple and problem-adapted on-line tools make it easy for students to do data analysis. Often, however, the limitations due to the simplicity are severe in some cases. If data are also available in a standard
format that can be imported into a data analysis software or a spreadsheet this is a valuable supplement. The core of our analysis can be found in Data analysis in the curriculum where we first of all analyze the role of data analysis in the current project and which type and support is provided for data analytical activities. The supports for data analysis that we look for are software tools, subject matter knowledge for data interpretation, data analytical and statistical knowledge and strategies, exemplary data analysis and expected answers. In general, we found that most projects could improve their sites if they added data analytical and statistical knowledge and strategies to their material. Project authors seem to underestimate the need for this and often seem to think that process goals related to the process of scientific method and discovery are already sufficient. A most important deficiency is that we can nearly never find more extensive prototypical data analyses that show the students how they might answer to the questions in a deeper way. We may be biased in this respect because we found that the data the projects provide are so interesting and rich and many interesting aspects of data analysis and of the subject matter can be found out by means of data exploration. The current projects do not yet exploit this potential. To show what we mean we provide our own example of data analysis for each project, often trying to explore a question that the project posed itself. We intend to use elementary methods of data analysis but apply them in a flexibel and interactive manner in the spirit of exploratory data analysis (John Tukey).. Our Summary is structured according to 1. The system; 2. Learning goals; 3. Available data; 4. Supports for data analysis; 5. Our own example of data analysis; 6. Summary from the perspective of data analysis. 1. We can conceive of all data sharing projects as refering to a system that is represented by a number of interacting variables and some context variables such as the water quality of a lake measured by several variables and context variables such as weather conditions. Or, the a road system that "produces" killed animals whose number and type depends on road type, weather conditions, time of the year and time of the day, for instance. 2. Learning goals refer to what students should learn about the respective system. These goals may be content goals, for instance some knowledge about water quality and basic chemical and physical processes, or typically rather process goals: students are encouraged and supported to behave like a scientist and participate in data collection and analysis as well as in the process of scientific research
from developing and questions and hypotheses up to answering some of the questions by means of data collection and analysis. 3. Available data briefly sumarize what we discuss in the chapters on data archive and data. 4. supports for data analysis contain software tools, subject matter knowledge for data interpretation, data analytical and statistical knowledge and strategies, exemplary data analysis and expected answers and summarizes findings from the chapters D. and E. from a more general perspective. Our own example of data analysis (part of E.) is summarized in point 5. The last point 6. provides a short overall evaluation. The fact that the summary differ in structure from the chapter structure is due to the need to enable comparison across projects (the aim of the summary) and to portrait each individual project and its specificities form the perspective of data analysis, which is the aim of the chapters B. to E.
3. The Five Technical Reports
1. Journey North 2. Estuary Net 3. Globe 4. Roadkill 5. Water on the Web 4. Acknowledgements We are very grateful to Cliff Konold and his co-workers at the University of Massachusetts who did a lot of work in improving the language and the understandability of our 5 technical reports. Anyway we are responsible for all errors and unclearnesses that do still exist.
FEASIBILITY STUDY To determine if their proposed capital fund raising campaign has a good chance of success, organizations should use fundraising feasibility study. The fundraising feasibility study should identify how much money the organization can possibly raise, how long the fund raising should take, and the costs that would be spent in conducting the campaign. The study should also identify potential donors, strength and weaknesses of the fundraising campaign, and recommendations on how the fundraising campaign should be conducted to get the best result. Through a good, objective fundraising feasibility study, an organization can learn what it needs to know before conducting a fundraising campaign, and whether all the critical factors for success are there or not.
ANALYTICAL REPORT The word “analysis” is actually very simple in meaning. It means to break something into its component parts, to see how it is put together. Still, for many students, it is a mystery word, a process that is something like an old-fashioned hamburger grinder. You put the meat in one end, and it comes out all chopped up on the other end. What happens in between the ends can sometimes seem mysterious, especially if a student is trying to get a handle on just what it is. Analytical reports call on you to answer questions, to ask why something happens, which product is the best, or is an idea good. Analytical reports call for research, interpretation and recommendation. And when you work within particular professional contexts, analysis often means very specific things involving your particular skill set and expertise. If you are writing a professional analysis from within your profession, you will very likely be called upon to apply a particular methodology to conduct your analysis. That, for you, is your meat grinder. A civil engineer is called on to analyze a situation to properly ventilate a room containing chemical fumes. Federal standards require the room air to change completely X number of times in an hour for the workers who must work in the room. The civil engineer uses certain professional skills to analyze the room dimensions, fan capacity, possible sites for fan placement, etc. Those are the parts that the problem is broken down into according to the expertise of that field. More than any other type of technical writing, analytical reports call on you to use critical thinking skills. They require you to analyze a problem, to analyze the work that has been done before on that problem, and to recommend a solution. Analytical reports also call for self-criticism and objectivity on your part to come to the best possible solution. They also require a frame of reference, if not a full-blown professional research methodology based in a discipline. It is difficult to conduct an analysis without expertise in something. On the other hand, many people have expertise they didn’t know they had. A group of first year students in a laptop computer pilot program were called on to analyze and write a report evaluating the program. They argued that they were only first year students and knew nothing about the program. But they were indeed experts. They had used their laptops and participated in the program for an entire year. They were the people most suited to conduct the analysis of the program.