IdeaWazaWiki - User contributions [en]

Spanish

2009-04-04T19:04:51Z

82.5.226.116:

{{spanish}}
{{nav|Topic:Spanish}}
{{merge|Topic:Spanish}}

Spanish language courses are one of the most wanted units on Wikiversity. On the other side, there is no progress in course development. The following brainstorming page is trying to find a wiki way, how to develop these courses: [[Spanish/Brainstorming]].

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This is a '''disambiguation page''' for [[w:en:Spanish language|Spanish language]] courses. It is operated by teachers from [[Topic:Spanish|Spanish Language Division]]. It offers courses and projects of Spanish for "foreign learners" (SFL), but also for "native speakers" (SNS). Courses are divided into six categories. They are numbered and they may reflect [[w:en:Common European Framework of Reference for Languages|Common European Framework of Reference for Languages]]. The exact categorization wasn’t already established. If you are not sure about your level of knowledge, you can ask one of these teachers to test you: [[Topic:Spanish/Active Participants#Spanish Language Teachers|teacher list]].

'''Important:''' note that for one level, more courses may be offered. They are about the same topic, but using different methodology.

'''Note:''' if you would like to help visit the [[Topic:Spanish|Spanish Language Divison]] and/or [[Spanish: An Introduction/Teaching tools]].

== Spanish for Native Speakers ==
* …
== Spanish for Foreign Learners ==
*'''Spanish 1 - beginners'''
**[[Spanish 1]] - Complete curriculum of Spanish for beginners.
**[[Spanish/Spanish One|Spanish One]]
***[[Spanish/Spanish One/SO-Lesson 1|Lesson 1]]
***[[Spanish/Spanish One/Vocabulary|Vocabulary]]
**[[Spanish: An Introduction]] - under construction

*'''Spanish 2 - lower-intermediate'''
**[[Spanish/Spanish Two|Spanish Two]] - not active
**[[Spanish 2]] - Developing curriculum of intermediate level Spanish.

*'''Spanish 3 - upper-intermediate'''
**[[Spanish/Spanish Three/Exact pronunciation|Exact pronunciation]]

*'''Spanish 4 - advanced'''
** …

== Projects ==
*[http://www.spanishdict.com/ Spanish to English Dictionary and Translation]
*[[Spanish/Bilingual Spanish-English Dictionary|Bilingual Spanish-English Dictionary]]
*[http://www.valodas.com/ valodas] - free cross-platform language learning software
*[http://www.livingspanish.com/ Free Online Spanish Coruses]

==Resources==
* [[Spanish/Resources/Las Palmas de Gran Canaria (audio)]]
* [[Spanish nouns]]

[[Category:Spanish Language Division]]

Aerospace engineering

2008-02-25T16:15:26Z

82.5.236.227: /* Aviation engines */

<center>
{| border=0 cellspacing=0 cellpadding=12 bgcolor="ccccff"
| ''' [[Topic:Aerospace Engineering/For editors|For editors]] '''
| ''' [[Topic:Aerospace Engineering/For lecturers|For lecturers]] '''
| ''' [[Topic:Aerospace Engineering/For students|For students]] '''
|}
</center>

Welcome to the Department of Aerospace Engineering.

Aerospace Engineering deals specifically with aircraft and spacecraft, as well as any other types of machines that can fly. Topics within aerospace engineering include, but are not limited to, aerodynamics, structural dynamics, fluid mechanics, orbital mechanics, flight dynamics, propulsion, and control systems. These topics can be applied to missiles, space structures, satellites, and all aspects related to atmosphere and space flight.

Aerospace engineers design, develop, and test aircraft, spacecraft, and missiles and supervise the production of these products. Those who work with aircraft are called aeronautical engineers, and those working specifically with spacecraft are astronautical engineers. Aerospace engineers develop new technologies for use in aviation, defense systems, and space exploration, often specializing in areas such as structural design, guidance, navigation and control, instrumentation and communication, or production methods. They also may specialize in a particular type of aerospace product, such as commercial aircraft, military fighter jets, helicopters, spacecraft, or missiles and rockets, and may become experts in aerodynamics, thermodynamics, celestial mechanics, propulsion, acoustics, or guidance and control systems.

== Areas of study ==
===General Prerequisites===
* [[Basic Mathematics]] - differentials, integrals, basic mechanics, vector algebra, matrices and matrix manipulation, total derivative (for mass and momentum equations, among others)
** [[wikibooks:Calculus | Calculus]]
** [[wikibooks:Differential Equations | Differential Equations]]
* Thermodynamics/Heat Transfer - Zereoth, First, Second and Third Laws, Enthalpy, Entropy, Clausius Inequality and ??, Steady State Equation, Modelling Gas Turbines/Engines, Conduction, Convection, Radiation (Black Body, Grey Body)
** [[wikibooks:Engineering Thermodynamics | Engineering Thermodynamics]]
* Circuits/Electronics
* Physics - Forces, Gravity Equation
** [[wikibooks:Physics Study Guide/Gravity | Gravity]]

=== Statics ===
* Structural Analysis
* Mechanics - Friction on a surface, Rolling bodies, Stability, Pure/Damped/Forced Harmonic Motion, Orbits (reaching orbit, geostationary point, changing orbit, escape velocity)
** [[wikibooks:Solid mechanics | Solid Mechanics]]

=== Fluid mechanics ===
* Aerodynamics - Derivation of shear stress on a fluid, perfect gas equation, Bernoulli equation, Langrangian and Eulerian reference frames, control volumes and control surfaces, Conservation of mass up to 3-d, balance of momentum equations up to 3-d, Aerofoils, Circulation, Mach Number & Renauld's Number, Laminar and turbulent flow, Propulsion & Turbomachinery
** [[wikibooks:Jet Propulsion/Aerodynamics | Aerodynamics]]
** [[wikibooks:Jet Propulsion | Jet Propulsion]]
** [[wikibooks:Rocket Propulsion:Contents | Rocket Propulsion]]

===Aircraft Structures===
* Basic Strength of Materials
* Aircraft Structures - Basic
* Aircraft Structures - Advanced
* Structural Analysis
* Recent Topics
=== Materials Science ===
* [[Material Classes]] - Metals, Ceramics, Composites, Polymers, Ionic and Covalents
* [[Material Microstructure]]
* [[Properties of Materials]] - Strength, Stiffness, Young's Modulus, Elasiticity and Modulus of Elasticity, Hardness, Toughness, Electrical Properties?
* [[Materials Selection]]
* [[Material Processes]] - Annealing, Quenching, Precipitaiton Hardening, Case Hardening
* [[Failure]] - Fatigue, Creep, Fracture, Case studies (aircraft)
* [[Composites]] - matrix and fibers, explanation of directional properties, case studes (carbon fibre, kevlar, fibreglass)
** [[wikibooks:Material science | Material Science]]

=== Aircraft Design ===
* Basic Aircraft Performance - Air density at altitudes, Perfect Gas equation,
* Dynamics and Control - Control Surfaces,

=== Aviation engines ===
*Hydraulic gas dynamics - characteristics of gas flowing through
*[[Theory of impeler machines]] - profiling blades of compressor and turbine, multistage compressor and multistage turbine
*Theory of jet engines - modeling turbojet engines
*Construction of jet engines

=== Aeroelasticity ===
*Introduction
*Static Aeroelasticity
*Dynamic Aeroelasticity
*Flight Testing

=== Rover Design ===
*[[Rover Mission Analysis and Design]]

==Department news==
The Aerospace Department is concerned with the technology that constitutes of Aeronautical and Astronautical engineering.

==Related news==
* '''April 26, 2007''' - [http://www.newscientist.com/channel/fundamentals/mg18925331.200-take-a-leap-into-hyperspace.html Paper on hyperdrive system wins award at conference and is examined by US Government researchers...][http://www.theregister.co.uk/2006/01/06/hyperdrive/]
* '''March 27, 2007''' - [http://www.physorg.com/news94233194.html NASA seeks research proposals.]
* '''March 20, 2007''' - [http://www.physorg.com/news93631842.html Private company to launch rocket.]

== External links ==
* [http://cafefoundation.org/v2/pav_home.php Personal Air Vehicle Page at Cafe Foundation (in affiliation with NASA)]
* [http://psas.pdx.edu/ Open avionics]
* [http://seattlepi.nwsource.com/business/130398_electplane11.html Boeing's electric plane using fuelcells.]
* [http://sourceforge.net/projects/openavionics/ OpenAvionics]
* [http://www.aiaa.org/ American Institute of Aeronautics and Astronautics]
* [http://aero.stanford.edu/adgprojects.html Projects at Standford's Aerodynamics Design Group]
===Books===
* [http://books.google.com/books?vid=ISBN1428996389&id=mViQar7gcfkC&dq=nanotechnology&as_brr=1 Research opportunities in advanced aerospace concepts]

*[[Wikibooks:Astrodynamics|Astrodynamics]]

[[Category:Aerospace engineering|!]]
[[Category:Engineering]]
[[Category:Departments]]

Website content writer

2007-08-31T18:01:00Z

82.5.49.154:

== Website Content Writers ==
Website content writers are persons who specialize in providing relevant text content to [[websites]]. Their expertise lies in adapting themselves to whatever particular website demands of them to compose. Most of their work centers on [[marketing]] particular product or service that sites are [[selling]] or endorsing.

== Functions of Content Writers: ==
There is a growing demand for content writers in the net because good content often translates into [[revenues]] for online businesses. [[Online]] entrepreneurs/owners depend on these writers for 2 major things:
* Content that would entice and engage visitors, so they stay browsing in the owner’s [[website]]. – operates in the premise that [[visitors]] who stay longer surfing a particular site [which offer some sort of product/service] will eventually become clients/ [[customers]]
* Content that is keyword smart- meaning, composition must contain relevant keywords/phrases [typed by users for searching] associated with the actual site for better [[Search Engine]] indexing and ranking
Added to these are user readability, usability, plus a touch of being up-to-date so [[company]] represented in the site conveys a sense of awareness on what is current and new in the [[industry]] they are operating in.

== Online Writers vs. Conventional Writers ==
Writing online is very different from composing /constructing content for printed materials, since surfers tend to scan instead of read in the [[internet]]. Skipping what they think is unnecessary [[information]] and hunting for what they really want.
Content writers must have the skills needed not only to stuff paragraphs with keywords for [[Search Engine Optimization]] purposes, but make sure their composition makes sense so they will be able to tap their [[target market]].

== Writers for Hire ==
In today’s world where [[internet]] presence often defines [[business]] survival, there are a variety of content writers out there that offer every company owner a chance to upgrade their [[sites]]. From freelancers to those who professionally engage in this kind of service, the net is filled with services offering this kind of [[expertise]]. Online writers need to conceptualize an idea in terms of the demands of search engine and as per the requirements of clients.

==Hiring the right content writer==
Since website content writers in the net are numerous, one must take into consideration several criteria in hiring the person for the job. This includes: 
• Relevant experiences in website content writing 
• Very good writing [[skills]] 
• Savvy [[research]] skills 
• [[Flexibility]] adaptability and availability 
There are many [[Information technology|IT]] companies that offer professional website content writing services, it’s just a matter of having business owners finding the writer that suits their company and personal style.

== Content Writing and off shore staffing ==
Website content writing [[service]] is one of the most productive services being [[outsourced]] nowadays. Web owners are finding it more and more profitable to hire off shore staff that professionally update their sites. Advantages include highly [[skilled]] professionals pre-screened to suit the client’s needs at [[cost]] effective and efficient terms.

== External links ==

* [http://www.wiredflame.com/ Wired Flame] - Copywriting services

[[Category:Information technology]]

Introduction to Statistics

2006-08-09T10:51:53Z

82.5.170.104:

{{nav3|Wikiversity|Wikiversity:School of Mathematics|School of Mathematics:Statistics}}

== Preamble ==
Statistics is permeated by probability. An understanding of basic probability is critical for the understanding of the basic mathematical underpining of statistics.

Most statistical procedures use probability to make a statement about the relationship between the independent variables and the dependent variables. Typically, the question one attempts to answer using statistics is that there is a relationship between two variables. To demonstrate that there is a relationship the experimenter must show that when one variable changes the second variable changes and that the amount of change is more than would be likely from mere chance alone.

There are two ways to figure the probability of an event. The first is to do a mathematical calculation to determine how often the event can happen. The second is to observe how often the event happens by counting the number of times the event could happen and also counting the number of times the event actually does happen.

The use of a mathematical calculation is when a person can say that the chance of the event rolling a one on a six sided die is one in six. The probability is figured by figuring the number of ways the event can happen and divide that number by the total number of possible outcomes. Another example is in a well shuffled deck of cards, what is the probability of the event of drawing a three. The answer is four in fifty two since there are four cards numbered three and there are a total of fifty two cards in a deck. The chance of the event of drawing a card in the suite of diamonds is thirteen in fifty two (there are thirteen cards of each of the four suites). The chance the event of drawing the three of diamonds is one in fifty two.

Sometimes, the size of the total event space, the number of different possible events, is not known. In that case, you will need to observe the event system and count the number of times the event actually happens versus the number of times it could happen but doesn't.

For instance, a warranty for a coffee maker is a probability statement. The manufacturer calculates that the probability the coffee maker will stop working before the warranty period ends is low. The way such a warranty is calculated involves testing the coffee maker to calculate how long the typical coffee maker continues to function. Then the manufacturer uses this calculation to specify a warranty period for the device. The actual calculation of the coffee maker's life span is made by testing coffee makers and the parts that make up a coffee maker and then using probability to calculate the warranty period.

== Experiments, Outcomes and Events ==

The easiest way to think of probability is in terms of experiments and their potential outcomes. Many examples can be drawn from everyday experience: On the drive home from work, you can encounter a flat tire, or have an uneventful drive; the outcome of an election can include either a win by candidate A, B, or C, or a runoff.

'''Definition:''' The entire collection of possible outcomes from an experiment is termed the ''sample space'', indicated as '''<math>\Omega</math>'''

The simplest (albeit uninteresting) example would be an experiment with only one possible outcome, say <math>A</math>. If we remember our set theory from elementary school, we can express the sample space as follows:

<math>\Omega = \{ A \} </math>

A more interesting example is the result of rolling a six sided dice. The sample space for this experiment is:

<math>\Omega = \{ 1,2,3,4,5,6 \}</math>

We may be interested in ''events'' in an experiment.

'''Definition:''' An ''event'' is some subset of outcomes from the ''sample space''

In the dice example, events of interest might include 
a) the outcome is an even number 
b) the outcome is less than three 

These events can be expressed in terms of the possible outcomes from the experiment: 
a) : <math> \{2,4,6\} </math> b) : <math> \{ 1,2 \}</math>

We can borrow definitions from set theory to express events in terms of outcomes. Here is a refresher of some terminology, and some new terms that will be important later: 
<math> \cup </math> represents the Union of two events 
[[Image:[http://en.wikipedia.org/wiki/Image:Venn_A_union_B.png]]] 
<math> \cap </math> represents the Intersection of two events 
[[Image:[http://en.wikipedia.org/wiki/Image:Venn_A_intersect_B.png]]] 
<math>\{\cdots\}^{c}</math> represents the complement of an event. For instance, "the outcome is an even number" is the complement of "the outcome is an odd number" in the dice example. 
<math> A \backslash B </math> represents ''difference'', that is, <math>A</math> ''but not'' <math>B</math>. For example, we may be interested in the event of drawing the queen of spades from a deck of cards. This can be expressed as the event of drawing a queen, but not drawing a queen of hearts, diamonds or clubs. 
<math>\varnothing</math> or <math>\{\}</math> represent an ''impossible event'' 
<math>\Omega</math> represents a ''certain event'' 
<math>A</math> and <math>B</math> are called ''disjoint events'' if <math>A\cap B = \varnothing</math>

== Probability ==
Now that we know what events are, we should think a bit about a way to express the likelihood of an event occuring. The classical definition of probability comes from the following. If we can perform our experiment over and over in a way that is repeatable, we can count the number of times that the experiment gives rise to event <math>A</math>. We also keep track of the number of times that we perform the same experiment. If we repeat the experiment a large enough number of times, we can express the probability of event <math>A</math> as follows: 
<math>P(A) = \frac{N_{A}}{N} </math> 
where <math>N_{A}</math> is the number of times event <math>A</math> occurred, and <math>N</math> is the number of times the experiment was repeated. As <math>N</math> approaches infinity, the fraction above approaches the true probability of the event <math>A</math>. The value of <math>P(A)</math> is clearly between 0 and 1. If our event is the ''certain event'' <math>\Omega</math>, then for each time we perform the experiment, the event <math>\Omega</math> is observed; <math>N_{\Omega} = N</math> and <math>P(\Omega)=1</math>. If our event is the ''impossible event'' <math>\varnothing</math>, we know <math>N_{\varnothing}=0</math> and <math> P(\varnothing) = 0</math>.

If <math>A</math> and <math>B</math> are ''disjoint events'', then whenever event <math>A</math> is observed, then it is impossible for event <math>B</math> to be observed simultaneously. Then 
<math>N(A\cup B) = N(A) + N(B)</math> 
Given our definition of probability, we can arrive at the following: 
<math>P(A\cup B) = P(A) + P(B)</math> 

At this point it's worth remembering that all events are not disjoint events. I was originally confused by events and outcomes, and this was the source of many misunderstandings. For events that are not disjoint, we end up with the following probability definition. 
<math> P(A\cup B) = P(A) + P(B) - P(A\cap B)</math> 
How can we see this from example? Well, let's consider drawing from a deck of cards. I'll define two ''events'': "drawing a Queen", and "drawing a Spade". We can tell off the bat that these are not disjoint events, because you can draw a queen that is also a spade. There are four queens in the deck, so if we perform the experiment of drawing a card, putting it back in the deck and shuffling (what statisticians refer to as ''sampling with replacement'', we will end up with a probability of <math>\frac{1}{13}</math> for a queen draw. By the same argument, we obtain a probability for drawing a spade as <math>\frac{1}{4}</math>. The expression <math>P(A\cup B)</math> here can be translated as "the chance of drawing a queen or a spade". If we assume naively (as I used to) that for this case <math>P(A\cup B) = P(A) + P(B)</math>, we can simply add our probabilities together for "the chance of drawing a queen or a spade" as <math>\frac{1}{13}+\frac{1}{4}</math>. If we were to gather some data experimentally, we would find that our results would differ from the prediction -- the probability observed would be slightly less than <math>\frac{1}{13}+\frac{1}{4}</math>. Why? Because we're counting the queen of spades twice in our expression, once as a spade, and again as a queen. We need to count it only once, as it can only be drawn with probability of <math>\frac{1}{52}</math>. [[Still confused?]] 

'''Proof:''' If <math>A</math> and <math>B</math> are not disjoint, we have to avoid the double counting problem by exactly specifying their union. 
<math>A \cup B = A \cup (B \backslash A) </math> so 
<math>P(A \cup B) = P(A \cup (B \backslash A)) </math> 
<math>A</math> and <math>B \backslash A</math> are disjoint sets. We can then use the definition of disjoint events from above to express our desired result: 
<math>P(A \cup B) = P(A) + P(B \backslash A)</math> 
We also know that 
<math>P(B \backslash A) = P(B) - P(B\cap A)</math> 
so 
<math> P(A \cup B) = P(A) + P(B) - P(B\cap A)</math> 
Whew! Our first proof. I hope that wasn't too [[dry]].

== Conditional Probability ==
Many events are conditional on the occurance of other events. Sometimes this coupling is weak. One event may become more or less probable depending on our knowledge that another event has occured. For instance, the probability that your friends and relatives will call asking for money is likely to be higher if you win the lottery. In my case, I don't think this probability would change.

Let's get formal for a second and remember our original definition of probability. 
<math>P(A) = \frac{N_{A}}{N}</math>
Consider an additional event <math>B</math>, and a situation where we are only interested in the probability of the occurance of <math>A</math> when <math>B</math> occurs. A way at this probability is to perform a set of experiments (''trials'') and only record our results when the event <math>B</math> occurs. In other words 
<math> \frac{N_{A\cap B}}{N_{B}} </math> 
We can divide through on top and bottom by <math>N</math> the total number of trials to get <math>P(A\cap B)/P(B)</math>. We define this as ''''conditional probability'''': 
<math>P(A|B) = \frac{P(A\cap B)}{P(B)}</math> 
which when spoken, takes the sound "probability of <math>A</math> given <math>B</math>."

[[/A Totally Confusing Problem that Shows how Difficult it is to Conquer Intuition/]]

=== Bayes' Law ===

An important theorem in statistics is '''Bayes' Law''', which states that 
<math> P(B|A) = \frac{P(A|B)P(A)}{P(B)}</math>, 
It is easy to prove. We start with identical expressions for <math>P(A\cap B)</math>. 
We know that: <math> P(A\cap B) = P(B\cap A) </math>, 
<math> P(A\cap B) = \frac{P(A|B)}{P(B)}</math>, and 
<math> P(B\cap A) = \frac{P(B|A)}{P(A)}</math>. 
Since <math> P(A\cap B) = P(B\cap A) </math>, 
<math>\frac{P(A|B)}{P(B)} = \frac{P(B|A)}{P(A)}</math>. 
A Simple rearrangement of above line gives us '''Bayes' Law'''.

== Independence ==

Two events <math>A</math> and <math>B</math> are called ''independent'' if the occurence of one has absolutely no effect on the probability of the occurence of the other. Mathematically, this is expressed as: 
<math>P(A\cap B) = P(A)P(B)</math>. 

== Random Variables ==

It's usually possible to represent the outcome of experiments in terms of integers or real numbers. For instance, in the case of conducting a poll, it becomes a little cumbersome to present the outcomes of each individual respondant. Let's say we poll ten people for their voting preferences (Republican - R, or Democrat - D) in two different electorial districts. Our results might look like this: 
<math>\{RRRDRRDRRR\}</math> and <math>\{DDDDDRDDDD\}</math> 
But we're probably only interested in the overall breakdown in voting preference for each district. If we assign an integer value to each outcome, say 0 for Democrat and 1 for Republican, we can obtain a concise summary of voting preference by district simply by adding the results together.

== Discrete and Continuous Random Variables ==
There are two important subclasses of random variables: discrete random variable (DRV) and continuous random variable (CRV).
Discrete random variables take only countably many values. It means that we can list the set of all possible values that a discrete random variable can take, or in other words, the number of possible values in the set that the variable can take is finite. If the posible values that a DRV X can take are a0,a1,a2,...an, the probability that X takes each is p0=P(X=a0), p1=P(X=a1), p2=P(X=a2),...pn=P(X=an). All these probabilites are greater than or equal zero.

For continuous random variables, we cannot list all possible values that a continuous variable can take because the number of values it can take is extremely large. It means that there is no use to calculate the probability of each value seperately because the probability that the variable takes a particular value is extremely small and can be considered zero P(X=x)=0).

== Distribution Functions ==

== Expectation Values ==

[[Category:School of Mathematics]]