OpenLLM-France/wikimedia huggingface dataset:
# HKDSE Geography/E2/Solutions to Sandstorms
## Planting Trees
### Afforestation

Large-scale afforestation projects can allow tree roots to bind soil particles together to better resist wind erosion and prevent sandstorms. The trees can also reduce wind speed. Green belts/shelterbelts/windbreaks can achieve this. If not, grass can be planted to gold the soil. The Three North Shelterbelt Programme is an example., spanning 4 million sq. km of northern China.

### Alternative: Straw Checkerboards

In arid areas, afforestation is ineffective since trees will die easily and are hard to grow. Straw checkerboards are used in Northwest China to check the spreading of deserts.

### Grain-To-Green

Returning farmlands and pasturelands to woodlands can also increase vegetation cover. Farmers are subsidised to grow cash crops and practise crop rotation in humid areas, and created eco-tourism jobs as well.

## Warning and Monitoring System

Remote sensing is used to monitor the development of sandstorms and warning signals are hoisted if necessary.

## East-to-West Water Transmission Project

To combat desertification in Xinjiang and Gansu, sea water from the Bo Hai is transported to Lop Nur in the Tarim Pendi in Xinjiang. More evaporation and condensation occur, effectively controlling desert spreading.

## Effectiveness

* Population growth of ethnic minorities has hindered its success. They are growing in numbers exceeding the carrying capacity of land.
** Xinjiang: Land can only support 7 people/km but population density is 10 people/km
* There is great economic burden on the government.
* Illegal devegetation remains serious.
* The arid climate is unfavourable for tree growth.
# Econometric Theory/Inference

Inference is essentially the process of creating a hypothesis of the parameters that describe a population by testing the sample parameters (such as ${\hat {\alpha }}$ and ${\hat {\beta }}$) that we already have from a sample of the population.
For example, you have a sample of size N, and you have created a model that can be used to predict changes in the units of the sample. The parameters of this model would be estimates of the actual parameters (such as $\alpha$ and $\beta$) for the entire population. Inference is the process used to determine, statistically, what the parameters would be for the whole population.
There are two basic ways of checking estimates for the purposes of statistical inference
* Interval estimation
* Hypothesis testing
To successfully use these methods, one must know two things:
* The probability distribution of the true parameter values ($\alpha$ and $\beta$) must be known.
* The formulae used must use data that can be found in the sample data (we can't successfully use unknown parameters in a function).
# Econometric Theory/Important Terms and Concepts of Regression Analysis

In the Introduction chapter, it was mentioned that regression is the tool of econometrics. Here, some important terms and concepts are introduced.
# Econometric Theory/Inverse of a square Matrix

The inverse of a matrix multiplied by the original matrix yields the identity matrix, P$^{-1}$P=I.
# Econometric Theory/Heteroskedasticity

One of our CLR assumptions for linear regression is that our disturbance terms are homoscedastic, meaning they have equal scatter ($Var(\epsilon )=\sigma ^{2}$). However, there are times that regressions end up with heteroscedastic disturbance terms, meaning the scatters are unequal ($Var(\epsilon )=\sigma _{i}^{2}$).

## Causes of Heteroscedasticity

Heteroscedasticity are more common in cross-sectional data than in time series. It is usually due to a scale or size factor.
Example: In basic Keynesian economics, we assume that savings and income are determined by wealth and income. Agents that have more wealth and income are more likely to save, this will produce a hetroscedastic relationship. The main causes include: 1. Presence of outliers 2. Omission of variable 3. Skewness in distribution of regressors 4. Incorrect or wrong functional form

## Consequences of Heteroscedasticity

1) OLS Coefficients are still unbiased for true value. $E({\hat {\beta }})=\beta$
Unbiased coefficients depend on $E(\epsilon )=0,cov(x_{i},\epsilon _{i})=0$
So the regression is safe from heteroscedasticity. on this assumption.
2) OLS Coefficients are not efficient. There exists an alternative to the OLS Coefficient that has a smaller variance than the OLS one. $\exists _{\tilde {\beta }}st.var({\tilde {\beta }})<var({\hat {\beta }}^{OLS})$ where $E({\tilde {\beta }})=\beta$ and therefore, is more efficient.
3) Hypothesis tests, in the presence of heteroscedasticity of an OLS coefficient, based on the standard error of the coefficient are invalid.
Bias in estimated variance of OLS Estimators causes inefficiencies.
Recall: ${\hat {\beta }}^{OLS}=\beta +{\frac {\sum (x_{i}+{\bar {x}})\epsilon _{i}}{\sum (x_{i}+{\bar {x}})^{2}}}$
When ${\hat {\beta }}$ is unbiased, the second term goes to zero. However, with heteroscedasticity, the second term is not zero.
Derive $var({\hat {\beta }}^{OLS})$ when $var(\epsilon )=\sigma _{i}^{2}$ $var({\hat {\beta }}^{OLS})=var({\frac {\sum (x_{i}+{\bar {x}})\epsilon _{i}}{\sum (x_{i}+{\bar {x}})^{2}}})$ $=({\frac {1}{\sum (x_{i}-{\bar {x}})^{2}}})^{2}var(\sum (x_{i}-{\bar {x}})\epsilon )$ $=({\frac {1}{\sum (x_{i}-{\bar {x}})^{2}}})^{2}\sum var((x_{i}-{\bar {x}})\epsilon )$ $=({\frac {1}{\sum (x_{i}-{\bar {x}})^{2}}})^{2}\sum x_{i}-{\bar {x}})^{2}var(\epsilon )$ $=({\frac {1}{\sum (x_{i}-{\bar {x}})^{2}}})^{2}\sum x_{i}-{\bar {x}})^{2}\sigma _{i}^{2}$
We can represent the differences in variances in the error term by the matrix Z
$\sigma _{i}^{2}=Z\sigma ^{2}$
# Econometric Theory/Least Squares
## Definition of least squares

The comparison method called Least squares is based on the fact that differences can be positive or negative, but that the square of a real negative number is always positive. Therefore, if there are several differences, the sum of all the squares of these differences is an indication of how close items are, and the best "fit" is the one with the least sum of the squares of the differences, with this method of comparison being called Least squares.

## Least Squares Estimation

For example an experiment resulted in values 2,5,4 at times 0, 1, 2. Let x be the time, and y be the actual results obtained. First we will compare the y with the values, at the times x, of a straight line; call these values y1 given by y1=2+x, being 2,3 and 4
At time x=0 the difference d=y-y1=0, its square d2=0, and the sum of the d2 so far is 0 At time x=1 the difference d=y-y1=2, its square d2=4, and the sum of the d2 so far is 4 At time x=2 the difference d=y-y1=0, its square is 0, and the sum of all the d2 is 4.
Trying another straight line, y2=2.7+x gives differences 2-2.7=-0.7, squared 0.49; next is 5-3.7=1.3, squared 1.69, and 4-4.7=-0.7, squared 0.49. The total of the squares is 0.49+1.69+0.49=2.67, being less than 4 it is the least squares so far.
Now let us try a quadratic equation, y3=2+5x-2x(squared), with its values shown above. The sum of the squares of the differences here is 0, being the least of the squares, and therefore the best fit.
# Econometric Theory/Matrix Algebra
## Matrices

A matrix is an array of numbers arranged into rows and columns. Some examples of matrices are,
$A={\begin{bmatrix}2&4&6\\0&-5&7.105\\1&-3&2\end{bmatrix}},\quad B={\begin{bmatrix}-3&-4\\\pi &{\sqrt {2}}\end{bmatrix}},{\mbox{ and }}\,\,\,C={\begin{bmatrix}-3&-5&0&0\\-1&4.56&3.28&19\end{bmatrix}}.$
When describing matrices we indicate the number of rows first, then the number of columns. For example, the matrix $C$ with two rows and four columns is said to be a $2\times 4$ matrix.
It is standard notation to name matrices with capital letters and to use lower case letters with subscripts to identify particular entries in a matrix.
For example, to identify the entry in row 1 and column 3 of matrix $A$ we would write $a_{13}$. To indicate that this entry is a six we would write the equation $a_{13}=6$.
Two matrices are considered to be equal only if they are the same size and every pair of corresponding elements are equal.
A column matrix is a matrix with only one column. Similarly, a row matrix has only one row.

## Vectors

A vector is an object often defined by a long list of properties. However, for now we will avoid the more complicated definition, and just say that a vector is an ordered list of numbers. Later we will see that vectors can really be much more.
An ordered pair, $(x,y)$, that is used to identify a point in the plane can be considered to be a vector.
Similarly, an ordered triple, $(x,y,z)$ is a vector.
Obviously, row and column matrices can also be considered to be a vector.
It is common to name vectors using variables with arrows above.
For example, we might write ${\vec {v}}=(2,3,5,-4),{\mbox{ or }}{\vec {w}}={\begin{bmatrix}4\\5\\0\end{bmatrix}}$.
For the most part, it will convenient to think of vectors as column matrices.
# Econometric Theory/Matrix Differentiation

There are a few simple rules for matrix differentiation. These allow much econometrics to be done in matrix form, which can be simpler and far less cumbersome than using nested summation signs.
Differentiating an inner product with respect to a vector
Let a be a given column vector and let x be a column choice vector (a vector of values to be chosen). Their transposes can be denoted as a' and x' . Then the derivative of their inner product, which is a scalar, is a column vector:
    ∂a'x/∂x = ∂x'a/∂x = a.
Differentiating a quadratic form with respect to a vector
Let A be a matrix, either symmetric or non-symmetric, and consider the quadratic form x'Ax, which is itself a scalar. The derivative of this quadratic form with respect to the vector x is the column vector
    ∂x'Ax/∂x = (A+A')x.
But in econometrics, almost always the matrix in the quadratic form will be symmetric. If A is indeed symmetric, the formula can be simplified to
    ∂x'Ax/∂x = 2Ax.
Application to Ordinary Least Squares
Perhaps the most basic concept in econometrics is ordinary least squares, in which we choose the regression coefficients so as to minimize the sum of squared residuals (mispredictions) of the regression. Suppose the regression model is
    y = Xβ + e,
where y is an n×1 vector of observed values of the dependent variable, X is an n×k matrix in which each column is an n×1 vector of observed values of one of the k independent variables (k<n), β is the k×1 vector of estimated response coefficients to be chosen, and e is the n×1 vector of residuals.
The reader may confirm that the sum of squared residuals—the sum of the squares of the elements of e—is given by
    e'e = (y - Xβ)'(y - Xβ) = y'y - y'Xβ - β'X'y + β'X'Xβ = y'y - 2β'(X'y) + β'(X'X)β
where the last equality holds because y'Xβ is a scalar and thus is its own transpose. Note that (X'y) is a k×1 vector and (X'X) is a k×k matrix. Using the above rules for differentiation, we have
    ∂e'e/∂β = -2X'y + 2(X'X)β,
where we have used the fact that X'X is symmetric. When we express the first-order condition by equating this vector derivative to the zero vector, we obtain
    (X'X)β = X'y,
which can be solved for the choice vector β as
    β = (X'X)$^{-1}$X'y.
This is the ordinary least squares estimator. Note that so long as X has full column rank, the expression (X'X)$^{-1}$ exists and (X'X) is positive definite, ensuring that the second-order conditions are satisfied.
# Econometric Theory/Introduction

The word econometrics means "economic measurement". The purpose of econometrics is to quantify and verify predictions from economic theory. It is a mixture of economic theory, mathematical economics, and statistics.
Apart from econometrics, there are two subjects closely related to econometrics. Firstly, mathematical economics is concerned with expressing economic theory in equations. It is algebra of the kind where variables can be subtracted, added, multiplied and divided in an equation. But it is not concerned with measurability or empirical verification. Secondly, we have economic statistics whose concern is to collect, process and present economic data. Many developed countries have government agencies whose job it is to collect and distribute economic data, such as the U.S. Bureau of Labor Statistics, which publishes data on unemployment, inflation, productivity, etc. The Bureau releases this information to the public and it is often used by other levels of government, social scientists, and interested laymen.
Lastly, it is important for econometricians to distinguish between observational data and experimental data. The use of observational data has three implications: firstly, the econometrician often uses data without knowledge of how the data were sampled; secondly, since the econometrician has no knowledge of how the data were generated, i.e. how people and companies behaved, there is uncertainty as to which economic or statistical model best represents these data; and thirdly, in order to obtain a suitable model that can be used for inference, econometrics is concerned with avoiding misspecification, or else the results of the model may be wrongly interpreted.

### Prerequisites

This Wikibook assumes that you have had some experience with the following topics:
* Set Theory
* Real Analysis
and are well-versed in the following:
* Statistics
* Linear Algebra
| Econometric Theory | Methodology of Econometrics → |
* ↑ This is the somewhat the view expressed by Judge et al. (1988, p. 5-6) and Gujarati (2003, p.5)

## Bibliography

* Judge; et al. (1988). Introduction to the Theory and Practice of Econometrics - 2nd ed. John Wiley & Sons. ISBN 0-471-62414-4. {{cite book}}: Explicit use of et al. in: |last= (help)
* Gujarati, D.N. (2003). Basic Econometrics, International Edition - 4th ed. McGraw-Hill Higher Education. ISBN 0-07-112342-3.
# History of the ODU Art Department/Peter Eudenbach

Conceptual artist Peter Eudenbach uses sculpture, installation, and video to explore the history of ideas while playing with our expectations of the commonplace. His work has been shown both nationally and internationally and he is a recent recipient of the Virginia Museum of Fine Arts Fellowship. In 2009 Eudenbach had solo exhibitions at the Kunstverein Grafschaft Bentheim in Neuenhaus, Germany and at the Taubman Museum of Art in Roanoke, Virginia.
Eudenbach lives and works in Norfolk, Virginia where he is an Associate Professor of Art at Old Dominion University.
# History of the ODU Art Department/Rick Nickel

Richard Nickel: Art Program Director
Richard Nickel (born October 1st, 1969 in Rochester, NY) is an American artist, who specializes in ceramics, and a ceramics professor at Old Dominion University.
BIOGRAPHY
Nickel obtained a Bachelor of Science in Art Education from State University College at Buffalo, NY in 1996. He went on to receive a Masters of Fine Art in Ceramics at Edinboro University of Pennsylvania in 2000.
Richard Nickel taught Ceramics and Art Education at Valley City University in North Dakota from 2000-2002. Currently, he works in both the Ceramics and Art Education programs at ODU. He started at ODU in 2002 as the Art Education Program Director and Ceramics Program Director. At ODU, Nickel is responsible for starting the Saturday Morning Art Classes at ODU. This was established in 2003.
His online portfolio and references can be viewed here:
http://richardnickel.wordpress.com/
WORKS
Nickel has been published in several Lark books on ceramics, 500 Tiles: An
Inspiring Collection of International Work Lark Books, 500 Animals In Clay, 500
Figures in Clay and 500 Bowls. His juried and invitational shows include, Tablets: text and Image in Clay Carbondale Cay Center, Colorado , Ink And Clay 34 Kellogg University Art Gallery at California Polytechnic, CA. Jurors: Darrel Couturier and Mark Greenfield, Forms & Shapes: Inspired by Architecture at Akar Gallery, Iowa City, IA , The George Ohr National Arts Challenge: Paul Soldner,The Ohr-O’Keefe Museum of Art, Biloxi, MS , he has been also awarded several Grants on research in Education and Ceramics, Faculty Innovator Grant, ODU Summer Research Fellowship. He has recently showed in the Summer of 2008 at Mayer Fine Art Gallery in Norfolk and the Fall of 2008 at the Contemporary Art Center in Virginia Beach. Also, recently, he was the curator for “Topics from the Earth” at the ODU Art Gallery in 2008.
# History of the World/Why should you study history?

Everyone lives in the now and their goal is to plan for the future in better ways. Of course, planning for the future is a bit difficult and you need to think a little bit. How should people plan for the future and how to make progress in the future?
People can predict the future 100% through history, which is the past life of people or even their ancestors.

## The Past Teaches Us About the Present:

History is a tool that, in addition to studying people in the past, can also examine and predict the future. Of course, you can study people's mistakes in history and then correct them. We will not repeat the mistake twice in the future.From history, past events that happened thousands of years ago can be used as a means to prevent it from happening again in the future. For example, the history of health was very important before now, and we were able to treat diseases that were known as terrible diseases before, but we can treat them well.In addition, we can expand science and by studying the history of science and knowing the scientists before us and by studying about their discoveries, we can expand the discoveries before us and we can invent science. History is also about the study of previous races that migrated, this study can be the main reason for the migration of ethnic groups and their racial composition and their language, and we can find out where the ethnic groups that live today originated from before. History also deals with the study of religions, which causes Which prophet does it belong to and when did this religion come into being and what events did it have.
# History of the ODU Art Department/Robert Wojtowicz
### Biography

Robert Wojtowicz was born December 12, 1961 in Philadelphia, PA. He grew up with four sisters and his parents were culturally minded individuals. Wojtowicz is a noted scholar of Lewis Mumford and finds Mumford a rewarding topic of research. Wojtowicz admires his concentration of teaching interdisciplinary studies and challenges himself to keep his sights high.
Originally he wanted to be an architect but was encouraged by a professor to study Art History because he demonstrated skills as a writer. His first job was the Art History position at ODU.

### Education

University of Pennsylvania, 1979-83 Masters of Arts, Bachelor of Arts 1984-90 Doctor of Philosophy. Columbia University, 1983-84 Master of Art History.

## Concentration

American Architecture and the history of Urban Planning.

## Teaching

Old Dominion University 1990

### Published Books

Lewis Mumford and American Modernism: Eutopian Theories for Architecture and Urban Planning.Cambridge, New York, and Melbourne: Cambridge University Press, 1996. Sidewalk Critic: Lewis Mumford's Writing on New York. New York: Princeton Architectural Press, 1998. Frank Lloyd Wright and Lewis Mumford: Thirty Years of Correspondence. Co-edited with Bruce Brooks Pfeiffer. New York: Princeton Architectural Press, 2001. Mumford on Modern Art in the 1930's. Berkeley, Los Angeles, and London: University of California Press, 2007.
# History of the World/What is History?

In the concept of history, it may have occurred to you to say (why should we study history?)
With this question, we get the answer that history is a lesson for learning and advice.
The exact meaning of history is as follows:
History is full of stories. It is caused by chaos or order or events that occur. By determining and recognizing time and ordering it, we can seek to understand the past in "facts". And from these stories we hope to be able to explain our decisions and our processes that are formed in our being. It can even be possible for us to have models and lessons that are a way to guide ourselves, our answers and we can distill today's challenges. History is the study of people, actions, decisions, interactions and behaviors. It is so persuasive because it depicts themes that reveal the human condition in all its guises and resonate through time: strength, weakness, corruption, tragedy, triumph...these themes are nowhere to be found. are not It is not clearer than political history, it is still the core. This is the field and the most important approaches to the study of history. However, political history has become out of fashion and subsequently discredited and wrongly demonized as obsolete and irrelevant. The result has been that the usefulness of ordering, explaining and distilling past lessons has been significantly removed.
The science of history can be in the form of wonderful stories that instruct us and study the history of people, a new narrative that is full of great characters and stories of turmoil and chaos where it is defeat or victory. Each generation adds its own chapters to history and reinterprets and finds new things in the same chapters already written.
In addition to giving advice, the science of history also gives us a sense of identity. By understanding (where do we come from? Who do we belong to?), with this question and research in the past, we can better understand who we are and where we came from. History in every field provides for our life and our existence. It helps us understand how things are and how we might approach the future.
History teaches us what it means to be human and highlights the great achievements and catastrophic mistakes of the human race. History also teaches us by example and gives us pointers on how to better organize and manage our societies for the benefit of all.