11(1) 6次 参考文献(23条) 1.Chen Yongzong;Jing Ke;Cai Qiangguo Modern Erosion and Management 1988 2.Lal R Soil erosion by wind and water: problems and proposals 1988 3.Shi Nianhai Loess Plateau and evolution of its agro-forest distribution area 1981 4.Zhu Kezhen Preliminary study of climate change in the last 5000 years of China 1973(02) 5.Tang Keli;Cai Qing Relationship between soil erosional process and eco—environmental evolution on the Loess Plateau. Collected Papers on Environmental Evolution and
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Joshua Davis 11-15-13 Arth102 Japanese Images Arts all over the world are appreciated not only in their beauty but also in their significance to history since most arts‚ if not all‚ tells the story of a nation in the period by which they were made. Through these arts historians can compare the civilization of nations‚ they can tell which of them developed first and they can tell which nations interacted with each other .The purpose of this paper is to compare and contrast Chinese
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Math/Stat 394‚ Winter 2011 F.W. Scholz Central Limit Theorems and Proofs The following gives a self-contained treatment of the central limit theorem (CLT). It is based on Lindeberg’s (1922) method. To state the CLT which we shall prove‚ we introduce the following notation. We assume that Xn1 ‚ . . . ‚ Xnn are independent random variables with means 0 and 2 2 respective variances σn1 ‚ . . . ‚ σnn with 2 2 2 σn1 + . . . + σnn = τn > 0 for all n. 2 Denote the sum Xn1 + . . . + Xnn by Sn and observe
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As we know that women are always related with sexuality‚ many people use this theme as a main topic of their novels‚ articles or any other literatures; included Wei Hui – one of the controversial Chinese feminist writer – who put women and sexuality as her main topic in her forbidden novel‚ the Shanghai Baby. Having lived in a state of quasi-servitude for over two thousand years‚ Chinese women began to demand liberation from their serfdom within the family clan complex. They sought to outlaw arranged
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(a) In general‚ let X1 ‚ X2 ‚ . . . ‚ Xn be a random sample drawn from this distribution. Since 2 1 2 1 7 µ1 = E[X] = 0 · θ + 1 · θ + 2 · (1 − θ) + 3 · (1 − θ) = − 2θ. 3 3 3 3 3 we have θ= and the MME for θ is 7 1 − µ1 ‚ 6 2 7 1 θˆ = − X‚ 6 2 1∑ Xi . In this case‚ we have X = 3/2 and n i=1 n where X = 7 1 3 5 θˆ = − · = . 6 2 2 12 (b) The likelihood function of the sample (3‚ 0‚ 2‚ 1‚ 3‚ 2‚ 1‚ 0‚ 2‚ 1) is lik(θ) = f (3‚ 0‚ 2‚ 1‚ 3‚ 2‚ 1‚ 0‚ 2‚ 1|θ) ( )2 ( )3 ( )3 ( )2 2 1 2 1 = θ · θ · (1 −
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An Analysis of Transformational Leadership BSP045 Work Psychology B010898 Cheng Chen Introduction Since the early 1980s‚ there has been an explosion of interest on transformational leadership among scholars and managers. It is shown with evidence that the desire and effectiveness of transformational leadership style are universal (Den Hartog‚ et al.‚ 1999‚ and Bass‚ et al. 2006). This leadership style‚ as its name implies‚ is a process which tends to change and transform individuals (Northouse
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Assumptions Consider the univariate linear regression framework: Yi = β0 + β1 Xi + ui Until now‚ it was assumed that E (ui |Xi ) = 0‚ i.e. conditional mean independence. Let’s relax this assumption and allow the covariance between Xi and ui to be dierent from zero. Our problem here is that ui is not observed. Doing OLS yields inconsistent estimates (remember the OVB formula). In this case we refer to Xi as an endogenous variable. The way to get consistent estimates is to use an
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Jim Rice sat in the back seat of a black Mercedes S-class as it drove the streets of Handan. He referred to the city‚ roughly 450 KM south of Beijing‚ as “a small Chinese town of 8.9 million people”. While it wasn’t part of his immediate plans‚ Rice was here to form the ties necessary to set up a future poultry operation. Beside him sat the Vice Mayor for agriculture‚ his chaperone on a tour of the city designed to showcase the quality of its land and its animal husbandry practices. Looking ahead
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1‚ 2‚ . . . ‚ n and (xi ‚ yi ) : i = 1‚ 2‚ · · · ‚ n are sets of n pairs of numbers‚ then: n n n (ai xi + bi yi ) = i=1 i=1 ai x i + i=1 bi yi 2. FALSE If xi : i = 1‚ 2‚ . . . ‚ n is a set of n numbers‚ then: n n n n n (xi − x) = ¯ i=1 n i=1 2 x2 i − 2¯ x i=1 xi + i=1 x = ¯ i=1 2 x2 − n¯2 x i where x = ¯ 1 n i=1 xi 3. TRUE If xi : i = 1‚ 2‚ . . . ‚ n is a set of n numbers and a is a constant‚ then: n n a xi = a i=1 n i=1 xi = a n x ¯ where x = ¯ 1 n i=1 xi 4. FALSE If X and Y
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Histogram of b0 Figure 2: Histogram of b1 2 i b. As we have known‚ b1 = i i(Xi −X)2 = i (Xii −X)2i = i Ki Yi whereKi = Xi −X¯ 2 ¯ ¯ i i i (Xi −X) So‚ b1 is a linear combination of Yi . Since Yi has a normal distribution‚ b1 also follows a normal distribution. E(b1 ) = i Ki E(Yi ) = i Ki (β0 + β1 Xi ) = i Ki β0 + ( i Ki Xi )β1 ¯ i (Xi −X) =0 ¯ i Ki = (Xi −X)2 i i i i i i i =1 ¯ 2 = ¯ 2 i Ki X i = i (Xi −X) i (Xi −X) E(b1 ) = 0 + 1 ∗ β1
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