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 | Academically Gifted |  | How does my gift benefit my school?
|  | EEF raises money for District-wide needs. The Annual Giving Campaign will directly support EISD teacher positions and salaries on every campus for the 2006-2007 school year.
Summary of how 2005-2006 money was used on each campus:
Barton Creek, Cedar Creek, Eanes and Valley View: At each school, converted half-time librarian position to full time.
Bridge Point: Added part-time reading specialist and part-time Teacher's Assistant.
Forest Trail: Added part-time Reading Recovery specialist and part-time library aide.
HCMS: Added a full-time equivalent instructor to reduce class sizes across all core areas: English, Math, Science and Social Studies.
WRMS: Added a full-time 7th and 8th grade English instructor to reduce English class sizes.
Westlake: Added 3 full-time instructors for core subjects.
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|  | | Home Schooling | | | What does it mean to homeschool?
| | Homeschooling means different things to different people. For some families, homeschooling means duplicating school at home, complete with textbooks, report cards and regularly scheduled field trips. For others, homeschooling is simply the way they live their lives - children and adults living and learning together with a seamlessness that would challenge an observer to determine which was 'home' and which was 'school.' If you think of a kind of homeschooling continuum, with 'school at home' at one end, and 'learning and living completely integrated' on the other - you would find homeschoolers scattered along that line with every possible variation of what homeschooling could mean. |
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| | What are some of the benefits of homeschooling?
| | A wise man once said, "We can teach our children to have courage, faith and endurance; they can teach us to laugh, to sing, and to love." For many, the deepest and most abiding benefit of homeschooling is the claiming (or reclaiming) of their family. Homeschooling families spend incredible amounts of time together living, learning and playing. They have the opportunity to develop a depth of understanding and a commitment to the family that is difficult to attain when family members spend their days going in separate directions.
Many families like the flexibility homeschooling provides both parents and children. Children can learn about things they are interested in and at a time in their lives when they are ready to learn. No preconceived schedule forces them ahead or holds them back. Vacations and outings can be planned for times when the family is ready - and often when the crowds are smaller or the costs are lower. Children can learn about the 'real world' by being a part of it - no artificial settings to 'provide exposure.' Children can receive a superior education attuned specifically to their own needs, learning styles, personalities, and interests - at far less cost than that of a private or public school.
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| | How do I know which materials and resources to use? | | This is, perhaps, the most difficult question to answer - be prepared for your answer to change over time and be aware that you may make choices that won't work out. Before you think about what you need, think about what learning means to you. School curriculum and methodology have evolved to reflect an environment where 25 or 30 children learn at the behest of one adult. Curriculum developed by experts for this useage has been designed for ease of teaching, but not necessarily for sparking the interest of an individual child.
As a homeschooling family, you can accept as many or as few of these materials as you like. Some families like the ease and security of having a prepackaged curriculum, while others choose to make their own decisions about what is important to learn and what is useful and helpful in their daily lives. Discuss this with your children. What do they want to do? How do they learn best? Look at sample copies of materials before you choose. As homeschoolers, you will be in charge of your learning - take advantage of all the adventure has to offer!
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| | | Job Applications | | | What is the yearly payment if I subscribe e-commerce / shopping cart package with WorldPay?
| | For Shopping Cart, you will need to make only one time payment of RM 2500.00. You will need to pay for the hosting charges for subsequent years. In such case if you use W2K Basic Server, the rate will be RM 330 per year. As for WorldPay, you will need to pay them an annual fee of GBP 160.00 (RM 1120) if you open a trading account with them to accept major credit cards. - Updated: February 18, 2004 |
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| | How do I get a job with the Department of the Treasury? | | The Department of the Treasury does not have a centralized hiring office. Each of the Department's 13 bureaus work independently in hiring employees to work for them. The good news is that all Federal agencies are required to post their announcements to the general public on the Office of Personnel Management's USAJobs website. Persons interested in finding jobs at the Department of Treasury, or within a specific Treasury bureau, can search by agency or occupation to find a variety of positions. USAJobs also has a personal search agent called "USAJobs by email" that notifies you of job openings matching your specific search criteria.
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| | HOW DO I KNOW WHAT POSITIONS ARE OPEN? | | You can view open positions on our web site by going to our home page and clicking on Job Listings.
You can also take advantage of the City's Job Interest Card system. A Job Interest Card can be obtained from the receptionist at City Hall or you can call Human Resources to have one mailed to you. Job Interest Cards remain active for six months, and in the event of an opening, you will receive notification that applications are being accepted. You should request and fill out a separate Job Interest Card for any of the employment categories you're interested in (see list below). Indicate the desired position, and make sure you address the card to yourself on the front. Then drop the card off at City Hall or place it in an envelope and mail it to our City Hall address at 2330 McCulloch Boulevard North, Lake Havasu City, AZ 86403.
Accounting
Automotive Mechanics
Clerical/Computer
Community Development (Planning, Zoning & Building Inspection)
Dispatching (911 Emergency)
Engineering/Drafting
Firefighting
Law Enforcement
Maintenance (Streets, Water, Wastewater & Parks)
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| | | Statistics | | | How can I get an R-squared value when a Stata command does not supply one?
| | 1. The problem
Users often request an R-squared value when a regression-like command in Stata appears not to supply one.
2. Warning: caveat lector
This FAQ looks at the question generally and discursively. There is a practical kernel explaining something that you can usually do and that is often of some help. Nevertheless, the FAQ is no substitute for the technicalities that may be crucial for particular models.
3. Why is R-squared not supplied?
If Stata refuses to give you an R-squared, there may be a good explanation other than that the developers never got around to implementing it. Perhaps the R-squared does not seem to be a good measure for this model, on some technical grounds. You have to consult the literature or an expert to take this further, unless you are an expert, in which case you probably disagree with the other experts.
4. What you can usually do
There is usually something you can do for yourself: calculate the correlation between the observed response and the predicted response, and then square it. Here is the general idea illustrated:
. sysuse auto, clear
. regress weight length
. predict weightp if e(sample)
. corr weight weightp if e(sample)
. di r(rho)^2
Try it and see. Naturally, in this example, you get an R-squared from regress anyway, so you need not do this. But similarly, you can check that you get the same result, in both cases 0.8949, to 4 decimal places.
You can also use the correlation coefficient itself, which here we will call R.
Two crucial details to note:
The predicted response must be on the same scale as the response, up to a linear transformation.
Use if e(sample) to make sure everything is done for the estimation sample only. (Here, the second if e(sample) is redundant, given the first, but it does no harm, especially if it reminds you which observations are being used.)
This way of doing things opens up some other elementary possibilities, which become obvious when pointed out but are often overlooked. You can now get a basic graph of observed versus predicted responses, such as
. twoway scatter weight weightp || function y = x, ra(weightp) clpat(dash)
Sometimes this graph makes it clearer why you got a surprising value of R-squared. Similarly, you could calculate residuals and plot against the predicted responses. Such graphs can always be drawn, whatever the complexities of the model, and they can be useful.
5. Positive virtues
It may be worth reminding ourselves of some positive virtues of R-squared (or R). In particular, Zheng and Agresti (2000) discuss the correlation between the response and the fitted response as a general measure of predictive power for generalized linear models (GLMs), and some of their arguments carry over to other classes of models. This measure has the advantage of referring to the original scale of measurement, of applying to all types of GLMs, and of being familiar to many users of statistics. Preferably, it should be used as a comparative measure for different models applied to the same dataset, given that restrictions on values of the response may imply limitations on its value (e.g., Cox and Wermuth 1992).
For an arbitrary GLM, this correlation is invariant under a location-scale transformation. It is the positive square root of the average proportion of variance explained by the predictors. However, again for an arbitrary GLM, it need not equal the positive square root of other definitions of R-squared (as will be discussed in a moment); and it need not be monotone increasing in the complexity of the predictors, although in practice that is common. The correlation is necessarily sensitive to outliers.
6. Beware varieties of (pseudo) R-squared
For many models, especially those with categorical responses, there are frequently several different supposed approximations or analogues to R-squared. Often they are labeled "pseudo". Beware that they typically do not agree, even roughly. You need to look at the literature in your field and to realize that software and papers may often be unclear about precisely what was calculated. Long and Freese (2003, 91–94) and Hardin and Hilbe (2001, 45–49) are excellent sources of guidance on the animals in the zoo.
Thus, if you do this after logit, you will find that the squared correlation between observed and predicted is not what logit reports as pseudo–R-squared (the formula for pseudo–R-squared is documented in [R] maximize).
7. A single figure of merit only
Even if you now have an R-squared, it is only a single figure of merit. Resist the temptation to use it as a weapon or as a comforter. Your R-squared may be high because your model codifies tautology or truism. Predicting today's temperature from yesterday's temperature would get you a high R-squared and might be a practical model for some purposes, but it is not a contribution to science at this time. Alternatively, your R-squared may be low, but no indictment of your model, if the field is refractory and your dataset is problematic. As R-squared never decreases as you add covariates (predictors), a high R-squared may go with a model that on scientific or statistical grounds has too many covariates.
There is likely to be a great deal of information about the limitations of the model, with implications for how it can be improved, in the detailed estimation results and residuals you can usually get from Stata, including graphical as well as numeric output. There is almost no such information in an R-squared.
8. A descriptive measure only
Even if you now have an R-squared, it is at best a descriptive measure. It considers only the information on which it is based, no more and no less, and says nothing about the structure of the data in any sense (e.g., dependence or cluster structure). If you attempt to make inferences based on R-squared, or on R, they may be highly fragile, unless somehow they respect the character of the model. This applies to bootstrap and jackknife work as well.
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