Multivariate Statistics
|
|
- Morgan Horn
- 5 years ago
- Views:
Transcription
1 Multivariate Statistics Chapter 3: Principal Component Analysis Pedro Galeano Departamento de Estadística Universidad Carlos III de Madrid Course 2017/2018 Master in Mathematical Engineering Pedro Galeano (Course 2017/2018) Multivariate Statistics - Chapter 3 Master in Mathematical Engineering 1 / 45
2 1 Introduction 2 Principal component analysis 3 Normalized principal component analysis 4 Principal component analysis in practice Pedro Galeano (Course 2017/2018) Multivariate Statistics - Chapter 3 Master in Mathematical Engineering 2 / 45
3 Introduction Suppose that we have a data matrix X with dimension n p. A central problem in multivariate data analysis is the curse of dimensionality: if the ratio n/p is not large enough, some problems might be intractable. For example, assume that we have a sample of size n from a p-dimensional random variable following a N(µ x, Σ x ) distribution. In this case, the number of parameters to estimate is p + p(p + 1)/2. For instance, for p = 5 and p = 10, there are 20 and 65 parameters, respectively. Thus, the larger p, the larger number of observations we need to obtain reliable estimates of the parameters. Pedro Galeano (Course 2017/2018) Multivariate Statistics - Chapter 3 Master in Mathematical Engineering 3 / 45
4 Introduction There are several dimension reduction techniques that try to answer the same question: Is it possible to describe with accuracy the values of p variables with a smaller number r < p of new variables? We are going to see in this chapter principal component analysis. Next chapter is devoted to factor analysis. Pedro Galeano (Course 2017/2018) Multivariate Statistics - Chapter 3 Master in Mathematical Engineering 4 / 45
5 Introduction As mentioned before, the main objective of principal component analysis (PCA) is to reduce the dimension of the problem. The simplest way of dimension reduction is to take just some variables of the observed multivariate random variable x = (x 1,..., x p ) and to discard all others. However, this is not a very reasonable approach since we loss all the information contained in the discarded variables. Principal component analysis is a flexible approach based on a few linear combinations of the original (centered) variables in x = (x 1,..., x p ). The p components of x are required to reproduce the total system variability. However much of the variability of x can be accounted for a small number of r < p of principal components. If so, there is almost as much information in the r principal components as there is in the original p variables contained in x. Pedro Galeano (Course 2017/2018) Multivariate Statistics - Chapter 3 Master in Mathematical Engineering 5 / 45
6 Introduction The general objective of PCA is dimension reduction. However, PCA is a powerful method to interpret the relationship between the univariate variables that form x = (x 1,..., x p ). Indeed, a PCA often reveals relationships that were not previously suspected and thereby allows interpretations that would not ordinarily results. It is important to note that a PCA is more of a means to an end rather than an end in themselves, because they frequently serve as intermediate steps in much larger investigations. As we shall see, principal components depend solely on the covariance (or correlation) matrix of x. Therefore, their development does not require a multivariate Gaussian assumption. Pedro Galeano (Course 2017/2018) Multivariate Statistics - Chapter 3 Master in Mathematical Engineering 6 / 45
7 Principal component analysis Given a multivariate random variable x = (x 1,..., x p ) with mean µ x and covariance matrix Σ x, the principal components are contained in a new multivariate random variable of dimension r p given by: z = A (x µ x ) where A is a certain p r matrix whose columns are p 1 vectors a j = (a j1,..., a jp ), for j = 1,..., r. Therefore, z = (z 1,..., z r ) is a linear transformation of x given by the r linear combinations a j, for j = 1,..., r. Pedro Galeano (Course 2017/2018) Multivariate Statistics - Chapter 3 Master in Mathematical Engineering 7 / 45
8 Principal component analysis Consequently: E (z j ) = 0 Var (z j ) = a jσ x a j Cov (z j, z k ) = a jσ x a k for j, k = 1,..., r. Pedro Galeano (Course 2017/2018) Multivariate Statistics - Chapter 3 Master in Mathematical Engineering 8 / 45
9 Principal component analysis In particular, among all the possible linear combinations of the variables in x = (x 1,..., x p ), the principal components are those that simultaneously verifies the following two properties: 1 The variances Var (z j ) = a jσ xa j are as large as possible. 2 The covariances Cov (z j, z k ) = a jσ xa k are 0, implying that the principal components of x, i.e., the variables in z = (z 1,..., z r ), are uncorrelated. Next, we formally derive the linear combinations a j, for j = 1,..., r that leads to the PC s. Pedro Galeano (Course 2017/2018) Multivariate Statistics - Chapter 3 Master in Mathematical Engineering 9 / 45
10 Principal component analysis Assume that the covariance matrix Σ x have a set of p eigenvalues λ 1,..., λ p with associated eigenvectors v 1,..., v p. Then, the first principal component corresponds to the linear combination with maximum variance. In other words, the first PC corresponds to the linear combination that maximizes Var (z 1 ) = σ 2 z 1 = a 1 Σ xa 1. However, it is clear that a 1 Σ xa 1 can be increased by multiplying any a 1 with some constant. To eliminate this indeterminacy, it is convenient to restrict attention to coefficient vector of unit length, i.e., we assume that a 1 a 1 = 1. The following linear combinations are obtained with a similar argument but adding the property that they are uncorrelated with the previous ones. Pedro Galeano (Course 2017/2018) Multivariate Statistics - Chapter 3 Master in Mathematical Engineering 10 / 45
11 Principal component analysis Therefore, we define: First principal component= arg max s.t. a 1 a1=1 a 1Σ x a 1 Second principal component= arg max a 2Σ x a 2 s.t. a 2 a2=1, a 1 Σx a2=0 r-th principal component=. arg max s.t. a r ar =1, a r Σx a k =0, for k<r a r Σ x a r Pedro Galeano (Course 2017/2018) Multivariate Statistics - Chapter 3 Master in Mathematical Engineering 11 / 45
12 Principal component analysis The first problem can be solved with the Lagrange multiplier method as follows. Let: Then, M = a 1S x a 1 β 1 (a 1a 1 1) M a 1 = 2Σ x a 1 2β 1 a 1 = 0 Σ x a 1 = β 1 a 1 Therefore, a 1 is an eigenvector of Σ x and β 1 is its corresponding eigenvalue. Which ones? Multiplying by a 1 in the last expression, we get: σ 2 z 1 = a 1Σ x a 1 = β 1 a 1a 1 = β 1 As σ 2 z 1 should be maximal, β 1 corresponds to the largest eigenvalue of Σ x (β 1 = λ 1 ) and a 1 is its associated eigenvector (a 1 = v 1 ). Pedro Galeano (Course 2017/2018) Multivariate Statistics - Chapter 3 Master in Mathematical Engineering 12 / 45
13 Principal component analysis The second principal component is given by: Second principal component= arg max a 2Σ x a 2 s.t. a 2 a2=1, a 1 Σx a2=0 where a 1 = v 1, the eigenvector associated with the largest eigenvalue of the covariance matrix Σ x. Therefore, Σ x v 1 = λ 1 v 1, so that: a 1Σ x a 2 = λ 1 v 1a 2 = 0 Following the reasoning for the first principal component, we define: M = a 2Σ x a 2 β 2 (a 2a 2 1) Then, M a 2 = 2Σ x a 2 2β 2 a 2 = 0 Σ x a 2 = β 2 a 2 Pedro Galeano (Course 2017/2018) Multivariate Statistics - Chapter 3 Master in Mathematical Engineering 13 / 45
14 Principal component analysis Therefore, a 2 is an eigenvector of Σ x and β 2 is its corresponding eigenvalue. Which ones? Multiplying by a 2 in the last expression, σ 2 z 2 = a 2Σ x a 2 = β 2 a 2a 2 = β 2 As σz 2 2 should be maximal, β 2 corresponds to the second largest eigenvalue of Σ x (β 2 = λ 2 ) and a 2 is its associated eigenvector (a 2 = v 2 ). This argument can be extended for successive principal components. Therefore, the r principal components corresponds to the eigenvectors of the covariance matrix Σ x associated with the r largest eigenvalues. Pedro Galeano (Course 2017/2018) Multivariate Statistics - Chapter 3 Master in Mathematical Engineering 14 / 45
15 Principal component analysis In summary, the principal components are given by: z = V r (x µ x ) where V r is a p r orthogonal matrix (i.e., V r V r = I r and V r V r = I p ) whose columns are the first r eigenvectors of Σ x. The covariance matrix of z, Σ z, is the diagonal matrix with elements λ 1,..., λ r, i.e., the first r eigenvalues of Σ x. Therefore, the usefulness of the principal components is two-fold: It allows for an optimal representation, in a space of reduced dimensions, of the original observations. It allows the original correlated variables to be transformed into new uncorrelated variables, facilitating the interpretation of the data. Pedro Galeano (Course 2017/2018) Multivariate Statistics - Chapter 3 Master in Mathematical Engineering 15 / 45
16 Principal component analysis Note that we can compute the principal components even for r = p. Taking this into account, it is easy to check that the PC s verifies the following properties: for j, k = 1,..., p. E (z j ) = 0 Var (z j ) = λ j Cov (z j, z k ) = 0 Var (z 1 ) Var (z 2 ) Var (z p ) 0 p p Var (z j ) = Tr (Σ x ) = Var (x j ) j=1 j=1 p Var (z j ) = Σ x j=1 Pedro Galeano (Course 2017/2018) Multivariate Statistics - Chapter 3 Master in Mathematical Engineering 16 / 45
17 Principal component analysis In particular, note that the fourth property ensures that the set of p principal components conserve the initial variability. Therefore, a measure of how well the r-th PC explains variation is given by the proportion of variability explained by r-th PC is given by: λ r PV r = λ λ p r = 1,..., p Additionally, a measure of how well the first r PCs explain variation is given by the accumulated proportion of variability explained by the first r PCs is given by: APV r = λ λ r λ λ p r = 1,..., p Therefore, if most (for instance, 80% or 90%) of the total variability can be attributed to the first one, two or three principal components, then these components can replace the original p variables without much loss of information. Pedro Galeano (Course 2017/2018) Multivariate Statistics - Chapter 3 Master in Mathematical Engineering 17 / 45
18 Principal component analysis The covariance between the principal components and the original variables can be written as follows: Cov (z, x) = E [ z (x µ x ) ] = E [ V r (x µ x ) (x µ x ) ] = = V r E [ (x µ x ) (x µ x ) ] = V r Σ x Now, the singular value decomposition of Σ x is given by Σ x = V p Λ p V p, where Λ p is the diagonal matrix that contains the p eigenvalues of Σ x in decreasing order and V p is the matrix that contains the p associated eigenvectors of Σ x. Therefore: Cov (z, x) = V r V p Λ p V p = Λ r V r where Λ r is the diagonal matrix that contains the r largest eigenvalues of Σ x in decreasing order. In particular, note that Λ r = Σ z. Pedro Galeano (Course 2017/2018) Multivariate Statistics - Chapter 3 Master in Mathematical Engineering 18 / 45
19 Principal component analysis On the other hand, the correlation between between the principal components and the original variables can be written as follows: Cor (z, x) = Σ 1/2 z E [ z (x µ x ) ] Dx 1/2 = Σ 1/2 z V r Σ x Dx 1/2 where D x is a p p diagonal matrix whose elements are the variances in the main diagonal of Σ x. Now, replacing Σ x with V p Λ p V p and Σ z with Λ r : Cor (z, x) = Λ 1/2 r V r V p Λ p V pd x 1/2 = Λ 1/2 r V r Dx 1/2 because Λ 1/2 r V r V p Λ p V p = Λ 1/2 r V r. The correlations of the principal components and the variables often help to interpret the components as they measure the contribution of each individual variable to each principal component. Pedro Galeano (Course 2017/2018) Multivariate Statistics - Chapter 3 Master in Mathematical Engineering 19 / 45
20 Normalized principal component analysis One problem with principal components is that they are not scale-invariant because if we change the units of the variables, the covariance matrix of the transformed variables will also change. Additionally, if there are large differences between the variances of the original variables, then those whose variances are largest will tend to dominate the early components. In these circumstances, it is better first to standardize the variables. In other words, the principal components should only be extracted from the original variables when all of them have the same scale. Pedro Galeano (Course 2017/2018) Multivariate Statistics - Chapter 3 Master in Mathematical Engineering 20 / 45
21 Normalized principal component analysis Therefore, if the variables have different units of measurement, we define y = Dx 1/2 (x µ x ) as the univariate standardized original variables and obtain the PCs from y. For that, note that: and Cov (y) = E [yy ] = E E (y) = 0 r [ ] Dx 1/2 (x µ x ) (x µ x ) Dx 1/2 = = Dx 1/2 Σ x Dx 1/2 = ϱ x i.e., the covariance matrix of y is the correlation matrix of x. Pedro Galeano (Course 2017/2018) Multivariate Statistics - Chapter 3 Master in Mathematical Engineering 21 / 45
22 Normalized principal component analysis Consequently, the principal components of y should be obtained from the eigenvectors of the correlation matrix of x, denoted by v ϱ 1,..., v ϱ r, with associated eigenvalues λ ϱ 1 λϱ r 0. In particular, these are given by: z ϱ = (V ϱ r ) y = (V ϱ r ) D 1/2 x (x µ x ) where Vr ϱ = [v ϱ 1 v r ϱ ] is the r r matrix that contains the eigenvectors of the correlation matrix ϱ x. The PCs obtained in this way are called the normalized principal components. Pedro Galeano (Course 2017/2018) Multivariate Statistics - Chapter 3 Master in Mathematical Engineering 22 / 45
23 Normalized principal component analysis All the previous results apply, with some simplifications as the variance of the univariate standardized variables is 1. Therefore, the normalized PC s verifies the following properties: ( ) E z ϱ j = 0 ( ) Var z ϱ j = λ ϱ j ( ) Cov z ϱ j, zϱ k = 0 for j, k = 1,..., p. Var (z ϱ 1 ) Var (zϱ 2 ) Var ( zp ϱ ) 0 p ( ) Var z ϱ j = Tr (Σ y ) = Tr (ϱ x ) = r j=1 p Var j=1 ( ) z ϱ j = ϱ x Pedro Galeano (Course 2017/2018) Multivariate Statistics - Chapter 3 Master in Mathematical Engineering 23 / 45
24 Normalized principal component analysis For normalized PCs, the proportion of variability explained by r-th principal component is given by: PV ϱ r = λϱ r p Similarly, for normalized PCs, the accumulated proportion of variability explained by the first r principal components based is given by: PV ϱ r = λϱ λϱ r p Pedro Galeano (Course 2017/2018) Multivariate Statistics - Chapter 3 Master in Mathematical Engineering 24 / 45
25 Normalized principal component analysis Additionally, it is possible to show that the covariance between the principal components based on the standardized variables and the original variables can be written as follows: Cov(z ϱ, x) = (Vr ϱ ) D 1/2 Σ x Moreover, the correlation between the principal components based on the standardized variables and the original variables can be written as follows: Cor(z ϱ, y) = (Λ ϱ r ) 1/2 (V ϱ r ) Here, Λ ϱ r is the covariance matrix of z, denoted by Σ ϱ z, that is a r r diagonal matrix that contains the eigenvalues of ϱ x, λ ϱ 1,..., λϱ r. x Pedro Galeano (Course 2017/2018) Multivariate Statistics - Chapter 3 Master in Mathematical Engineering 25 / 45
26 Principal component analysis in practice In practice, one replace the population quantities with their corresponding sample counterparts based on the data matrix X of dimension n p. The principal component scores are the values of the new variables. If we use the sample covariance matrix to obtain the principal components, the data matrix that contains the principal component scores is given by: Z = X V Sx r where Vr Sx is the matrix that contains the eigenvectors of the sample covariance matrix S x linked with the r largest eigenvalues. Pedro Galeano (Course 2017/2018) Multivariate Statistics - Chapter 3 Master in Mathematical Engineering 26 / 45
27 Principal component analysis in practice On the other hand, if we use the sample correlation matrix to obtain the principal components, the data matrix that contains the principal component scores is given by: Z = YVr Rx = X D 1/2 V Rx where Vr R is the matrix that contains the eigenvectors of the sample correlation matrix R x linked with the r largest eigenvalues and D Sx is the diagonal matrix that contains the sample variances of the components of X. S x r Pedro Galeano (Course 2017/2018) Multivariate Statistics - Chapter 3 Master in Mathematical Engineering 27 / 45
28 Principal component analysis in practice Different rules have been suggested for selecting the number of components r for data sets: 1 Plot a graph of 1,..., p against λ 1,..., λ p (the scree plot): The idea is to exclude of the analysis those components associated with small values and that approximately the same size. 2 Select components until a certain proportion of the variance has been covered, such as 80% or 90%: This rule should be applied with caution because sometimes a single component picks up most of the variability, whereas there might be other components with interesting interpretations. 3 Discard those components associated with eigenvalues of less than a certain value such as the mean of the eigenvalues of the sample covariance or correlation matrix: Again, this rule is arbitrary: a variable that is independent from the rest usually accounts for a principal component and can have a large eigenvalue. 4 Use asymptotic results on the estimated eigenvalues of the covariance or correlation matrices used to derive the PCs. Pedro Galeano (Course 2017/2018) Multivariate Statistics - Chapter 3 Master in Mathematical Engineering 28 / 45
29 Illustrative example I Consider the following eight univariate variables measured on the 50 states of the USA: x1: population estimate as of July 1, 1975 (in thousands). x2: per capita income (1974) (in dollars). x3: illiteracy (1970, percent of population). x4: life expectancy in years ( ). x5: murder and non-negligent manslaughter rate per population (1976). x6: percent high-school graduates (1970). x7: mean number of days with minimum temperature below freezing ( ) in capital or large city. x8: land area in square miles. Pedro Galeano (Course 2017/2018) Multivariate Statistics - Chapter 3 Master in Mathematical Engineering 29 / 45
30 Illustrative example I Original dataset e+00 2e+05 4e+05 Population Income Illiteracy Life.Exp Murder HS.Grad Frost e+00 5e Area Pedro Galeano (Course 2017/2018) Multivariate Statistics - Chapter 3 Master in Mathematical Engineering 30 / 45
31 Illustrative example I Sx = The sample mean vector for the data is given by: x = ( , , 1.17, 70.87, 7.37, 53.10, , ) The sample covariance matrix is given by: Pedro Galeano (Course 2017/2018) Multivariate Statistics - Chapter 3 Master in Mathematical Engineering 31 / 45
32 Illustrative example I The eigenvectors of S x are the columns of the V Sx 8 matrix given by: V Sx = Note that the first eigenvector is associated with the last variable which is the one with largest sample variance and so on with the other ones. Pedro Galeano (Course 2017/2018) Multivariate Statistics - Chapter 3 Master in Mathematical Engineering 32 / 45
33 Illustrative example I The eigenvalues of S x are λ Sx 1 = , λ Sx 2 = , λ Sx 3 = , λ Sx 4 = , λ Sx 5 = 36.51, λsx 6 = 6.05, λsx 7 = 0.43 and λsx 8 = The proportion of variability explained by the components are 0.997, , , , , , and , respectively. Consequently, the first proportion of accumulated variability is 0.997, while the others are larger than Pedro Galeano (Course 2017/2018) Multivariate Statistics - Chapter 3 Master in Mathematical Engineering 33 / 45
34 Illustrative example I We standardize the data matrix. Therefore, we obtain eigenvalues and eigenvectors of the sample correlation matrix of the data set which is given by: R x = Pedro Galeano (Course 2017/2018) Multivariate Statistics - Chapter 3 Master in Mathematical Engineering 34 / 45
35 Illustrative example I The eigenvectors of R x are the columns of the V Rx 8 matrix given by: V Rx = The eigenvalues of R x are λ Rx 1 = 3.59, λ Rx 2 = 1.63, λ Rx 3 = 1.11, λ Rx 4 = 0.70, λ Rx 5 = 0.38, λ Rx 6 = 0.30, λ Rx 7 = 0.14 and λ Rx 8 = The proportion of variability explained by the components are 0.449, 0.203, 0.138, 0.088, 0.048, 0.038, and Consequently, the proportion of accumulated variability are 0.449, 0.653, 0.792, 0.881, 0.929, 0.967, and 1, respectively. Pedro Galeano (Course 2017/2018) Multivariate Statistics - Chapter 3 Master in Mathematical Engineering 35 / 45
36 Illustrative example I Scores PC PC2 PC PC4 PC PC6 PC PC8 Pedro Galeano (Course 2017/2018) Multivariate Statistics - Chapter 3 Master in Mathematical Engineering 36 / 45
37 Illustrative example I Screeplot Variances Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6 Comp.7 Comp.8 Pedro Galeano (Course 2017/2018) Multivariate Statistics - Chapter 3 Master in Mathematical Engineering 37 / 45
38 Illustrative example I In this case, we can select the first three principal components as they explain the 79% of the total variability and the mean of the eigenvalues of the sample covariance or correlation matrix is 1 (λ Rx 3 = 1.11 and λ Rx 4 = 0.70). The first component is given by: z 1 = 0.12 x x x x x x x x 8 The first principal component distinguishes between cold states with rich, longlived, and educated populations, from warm states with poor, short-lived, illeducated and violent states. Pedro Galeano (Course 2017/2018) Multivariate Statistics - Chapter 3 Master in Mathematical Engineering 38 / 45
39 Illustrative example I The second component is given by: z 2 = 0.41 x x x x x x x x 8 The second principal component distinguishes big and populated states with rich and educated, although violent states, from small and low populated states with poor and ill-educated people. The third component is given by: z 3 = 0.65 x x x x x x x x 8 The third principal component distinguishes populated states with rich and longlived populations from warm and big states that tends to be ill-educated and violent. Pedro Galeano (Course 2017/2018) Multivariate Statistics - Chapter 3 Master in Mathematical Engineering 39 / 45
40 Illustrative example I First and second principal component Second principal component Alaska California Texas New York Florida Arizona Georgia Virginia Louisiana Alabama New Mexico North Carolina Tennessee South Carolina Mississippi Kentucky Arkansas West Virginia Illinois Nevada Michigan Maryland Ohio Washington Colorado Pennsylvania New Jersey Hawaii Oregon Missouri Montana Wyoming Kansas Indiana Massachusetts Connecticut Minnesota Delaware Oklahoma Idaho Wisconsin Nebraska Utah Iowa North Dakota Rhode Island Maine New Hampshire South Dakota Vermont First principal component Pedro Galeano (Course 2017/2018) Multivariate Statistics - Chapter 3 Master in Mathematical Engineering 40 / 45
41 Illustrative example I First and third principal component Third principal component Louisiana Alabama South Carolina Mississippi Georgia Texas Tennessee North Carolina Arkansas Kentucky West Virginia Florida New Mexico New York Virginia Arizona First principal component California Hawaii PennsylvaniaOhio New Jersey Massachusetts Illinois Washington Michigan Connecticut Oregon Maryland Wisconsin Indiana Rhode Island Kansas Iowa Minnesota Missouri Oklahoma Nebraska Delaware North Utah Dakota Vermont Idaho New Hampshire South Colorado Dakota Maine Nevada Alaska Montana Wyoming Pedro Galeano (Course 2017/2018) Multivariate Statistics - Chapter 3 Master in Mathematical Engineering 41 / 45
42 Illustrative example I Second and third principal component Third principal component Hawaii Florida Massachusetts Pennsylvania New Jersey Ohio Washington Illinois Connecticut Michigan Wisconsin Oregon Rhode Island Iowa Minnesota Indiana Maryland Kansas Virginia Tennessee Oklahoma Nebraska Missouri Arizona Arkansas Kentucky North Delaware Carolina North Dakota Utah Louisiana Alabama West Vermont New Virginia Hampshire Idaho Georgia South Dakota Colorado Maine South Carolina Mississippi New Mexico Montana Wyoming Nevada New York Texas California Alaska Second principal component Pedro Galeano (Course 2017/2018) Multivariate Statistics - Chapter 3 Master in Mathematical Engineering 42 / 45
43 Illustrative example I Finally, the correlation between the principal components based on the standardized variables and the original ones are given by: Cor (z, x) = Pedro Galeano (Course 2017/2018) Multivariate Statistics - Chapter 3 Master in Mathematical Engineering 43 / 45
44 Chapter outline We are ready now for: Chapter 4: Factor Analysis Pedro Galeano (Course 2017/2018) Multivariate Statistics - Chapter 3 Master in Mathematical Engineering 44 / 45
45 1 Introduction 2 Principal component analysis 3 Normalized principal component analysis 4 Principal component analysis in practice Pedro Galeano (Course 2017/2018) Multivariate Statistics - Chapter 3 Master in Mathematical Engineering 45 / 45
Multivariate Statistics
Multivariate Statistics Chapter 4: Factor analysis Pedro Galeano Departamento de Estadística Universidad Carlos III de Madrid pedro.galeano@uc3m.es Course 2017/2018 Master in Mathematical Engineering Pedro
More informationMultivariate Analysis
Multivariate Analysis Chapter 5: Cluster analysis Pedro Galeano Departamento de Estadística Universidad Carlos III de Madrid pedro.galeano@uc3m.es Course 2015/2016 Master in Business Administration and
More informationMultivariate Statistics
Multivariate Statistics Chapter 6: Cluster Analysis Pedro Galeano Departamento de Estadística Universidad Carlos III de Madrid pedro.galeano@uc3m.es Course 2017/2018 Master in Mathematical Engineering
More informationNew Educators Campaign Weekly Report
Campaign Weekly Report Conversations and 9/24/2017 Leader Forms Emails Collected Text Opt-ins Digital Journey 14,661 5,289 4,458 7,124 317 13,699 1,871 2,124 Pro 13,924 5,175 4,345 6,726 294 13,086 1,767
More informationStandard Indicator That s the Latitude! Students will use latitude and longitude to locate places in Indiana and other parts of the world.
Standard Indicator 4.3.1 That s the Latitude! Purpose Students will use latitude and longitude to locate places in Indiana and other parts of the world. Materials For the teacher: graph paper, globe showing
More informationJakarta International School 6 th Grade Formative Assessment Graphing and Statistics -Black
Jakarta International School 6 th Grade Formative Assessment Graphing and Statistics -Black Name: Date: Score : 42 Data collection, presentation and application Frequency tables. (Answer question 1 on
More informationLecture 5: Ecological distance metrics; Principal Coordinates Analysis. Univariate testing vs. community analysis
Lecture 5: Ecological distance metrics; Principal Coordinates Analysis Univariate testing vs. community analysis Univariate testing deals with hypotheses concerning individual taxa Is this taxon differentially
More informationChallenge 1: Learning About the Physical Geography of Canada and the United States
60ºN S T U D E N T H A N D O U T Challenge 1: Learning About the Physical Geography of Canada and the United States 170ºE 10ºW 180º 20ºW 60ºN 30ºW 1 40ºW 160ºW 50ºW 150ºW 60ºW 140ºW N W S E 0 500 1,000
More informationLecture 5: Ecological distance metrics; Principal Coordinates Analysis. Univariate testing vs. community analysis
Lecture 5: Ecological distance metrics; Principal Coordinates Analysis Univariate testing vs. community analysis Univariate testing deals with hypotheses concerning individual taxa Is this taxon differentially
More information, District of Columbia
State Capitals These are the State Seals of each state. Fill in the blank with the name of each states capital city. (Hint: You may find it helpful to do the word search first to refresh your memory.),
More informationCorrection to Spatial and temporal distributions of U.S. winds and wind power at 80 m derived from measurements
JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 109,, doi:10.1029/2004jd005099, 2004 Correction to Spatial and temporal distributions of U.S. winds and wind power at 80 m derived from measurements Cristina L. Archer
More informationCooperative Program Allocation Budget Receipts Southern Baptist Convention Executive Committee May 2018
Cooperative Program Allocation Budget Receipts May 2018 Cooperative Program Allocation Budget Current Current $ Change % Change Month Month from from Contribution Sources 2017-2018 2016-2017 Prior Year
More informationCooperative Program Allocation Budget Receipts Southern Baptist Convention Executive Committee October 2017
Cooperative Program Allocation Budget Receipts October 2017 Cooperative Program Allocation Budget Current Current $ Change % Change Month Month from from Contribution Sources 2017-2018 2016-2017 Prior
More informationCooperative Program Allocation Budget Receipts Southern Baptist Convention Executive Committee October 2018
Cooperative Program Allocation Budget Receipts October 2018 Cooperative Program Allocation Budget Current Current $ Change % Change Month Month from from Contribution Sources 2018-2019 2017-2018 Prior
More informationAbortion Facilities Target College Students
Target College Students By Kristan Hawkins Executive Director, Students for Life America Ashleigh Weaver Researcher Abstract In the Fall 2011, Life Dynamics released a study entitled, Racial Targeting
More informationPreview: Making a Mental Map of the Region
Preview: Making a Mental Map of the Region Draw an outline map of Canada and the United States on the next page or on a separate sheet of paper. Add a compass rose to your map, showing where north, south,
More informationHourly Precipitation Data Documentation (text and csv version) February 2016
I. Description Hourly Precipitation Data Documentation (text and csv version) February 2016 Hourly Precipitation Data (labeled Precipitation Hourly in Climate Data Online system) is a database that gives
More informationOutline. Administrivia and Introduction Course Structure Syllabus Introduction to Data Mining
Outline Administrivia and Introduction Course Structure Syllabus Introduction to Data Mining Dimensionality Reduction Introduction Principal Components Analysis Singular Value Decomposition Multidimensional
More informationAdditional VEX Worlds 2019 Spot Allocations
Overview VEX Worlds 2019 Spot s Qualifying spots for the VEX Robotics World Championship are calculated twice per year. On the following table, the number in the column is based on the number of teams
More informationQF (Build 1010) Widget Publishing, Inc Page: 1 Batch: 98 Test Mode VAC Publisher's Statement 03/15/16, 10:20:02 Circulation by Issue
QF 1.100 (Build 1010) Widget Publishing, Inc Page: 1 Circulation by Issue Qualified Non-Paid Circulation Qualified Paid Circulation Individual Assoc. Total Assoc. Total Total Requester Group Qualified
More informationLast time: PCA. Statistical Data Mining and Machine Learning Hilary Term Singular Value Decomposition (SVD) Eigendecomposition and PCA
Last time: PCA Statistical Data Mining and Machine Learning Hilary Term 2016 Dino Sejdinovic Department of Statistics Oxford Slides and other materials available at: http://www.stats.ox.ac.uk/~sejdinov/sdmml
More informationMultivariate Classification Methods: The Prevalence of Sexually Transmitted Diseases
Multivariate Classification Methods: The Prevalence of Sexually Transmitted Diseases Summer Undergraduate Mathematical Sciences Research Institute (SUMSRI) Lindsay Kellam, Queens College kellaml@queens.edu
More informationA. Geography Students know the location of places, geographic features, and patterns of the environment.
Learning Targets Elementary Social Studies Grade 5 2014-2015 A. Geography Students know the location of places, geographic features, and patterns of the environment. A.5.1. A.5.2. A.5.3. A.5.4. Label North
More information2005 Mortgage Broker Regulation Matrix
2005 Mortgage Broker Regulation Matrix Notes on individual states follow the table REG EXEMPTIONS LIC-EDU LIC-EXP LIC-EXAM LIC-CONT-EDU NET WORTH BOND MAN-LIC MAN-EDU MAN-EXP MAN-EXAM Alabama 1 0 2 0 0
More informationIntercity Bus Stop Analysis
by Karalyn Clouser, Research Associate and David Kack, Director of the Small Urban and Rural Livability Center Western Transportation Institute College of Engineering Montana State University Report prepared
More informationPrintable Activity book
Printable Activity book 16 Pages of Activities Printable Activity Book Print it Take it Keep them busy Print them out Laminate them or Put them in page protectors Put them in a binder Bring along a dry
More informationOnline Appendix: Can Easing Concealed Carry Deter Crime?
Online Appendix: Can Easing Concealed Carry Deter Crime? David Fortunato University of California, Merced dfortunato@ucmerced.edu Regulations included in institutional context measure As noted in the main
More informationChapter. Organizing and Summarizing Data. Copyright 2013, 2010 and 2007 Pearson Education, Inc.
Chapter 2 Organizing and Summarizing Data Section 2.1 Organizing Qualitative Data Objectives 1. Organize Qualitative Data in Tables 2. Construct Bar Graphs 3. Construct Pie Charts When data is collected
More informationClub Convergence and Clustering of U.S. State-Level CO 2 Emissions
Methodological Club Convergence and Clustering of U.S. State-Level CO 2 Emissions J. Wesley Burnett Division of Resource Management West Virginia University Wednesday, August 31, 2013 Outline Motivation
More informationJAN/FEB MAR/APR MAY/JUN
QF 1.100 (Build 1010) Widget Publishing, Inc Page: 1 Circulation Breakdown by Issue Qualified Non-Paid Qualified Paid Previous This Previous This Total Total issue Removals Additions issue issue Removals
More informationSUPPLEMENTAL NUTRITION ASSISTANCE PROGRAM QUALITY CONTROL ANNUAL REPORT FISCAL YEAR 2008
SUPPLEMENTAL NUTRITION ASSISTANCE PROGRAM QUALITY CONTROL ANNUAL REPORT FISCAL YEAR 2008 U.S. DEPARTMENT OF AGRICULTURE FOOD AND NUTRITION SERVICE PROGRAM ACCOUNTABILITY AND ADMINISTRATION DIVISION QUALITY
More informationOffice of Special Education Projects State Contacts List - Part B and Part C
Office of Special Education Projects State Contacts List - Part B and Part C Source: http://www.ed.gov/policy/speced/guid/idea/monitor/state-contactlist.html Alabama Customer Specialist: Jill Harris 202-245-7372
More informationWhat Lies Beneath: A Sub- National Look at Okun s Law for the United States.
What Lies Beneath: A Sub- National Look at Okun s Law for the United States. Nathalie Gonzalez Prieto International Monetary Fund Global Labor Markets Workshop Paris, September 1-2, 2016 What the paper
More informationParametric Test. Multiple Linear Regression Spatial Application I: State Homicide Rates Equations taken from Zar, 1984.
Multiple Linear Regression Spatial Application I: State Homicide Rates Equations taken from Zar, 984. y ˆ = a + b x + b 2 x 2K + b n x n where n is the number of variables Example: In an earlier bivariate
More informationNorth American Geography. Lesson 2: My Country tis of Thee
North American Geography Lesson 2: My Country tis of Thee Unit Overview: As students work through the activities in this unit they will be introduced to the United States in general, different regions
More informationOsteopathic Medical Colleges
Osteopathic Medical Colleges Matriculants by U.S. States and Territories Entering Class 0 Prepared by the Research Department American Association of Colleges of Osteopathic Medicine Copyright 0, AAM All
More informationSummary of Natural Hazard Statistics for 2008 in the United States
Summary of Natural Hazard Statistics for 2008 in the United States This National Weather Service (NWS) report summarizes fatalities, injuries and damages caused by severe weather in 2008. The NWS Office
More informationHigh School World History Cycle 2 Week 2 Lifework
Name: Advisory: Period: High School World History Cycle 2 Week 2 Lifework This packet is due Monday, November 7 Complete and turn in on Friday for 10 points of EXTRA CREDIT! Lifework Assignment Complete
More informationRELATIONSHIPS BETWEEN THE AMERICAN BROWN BEAR POPULATION AND THE BIGFOOT PHENOMENON
RELATIONSHIPS BETWEEN THE AMERICAN BROWN BEAR POPULATION AND THE BIGFOOT PHENOMENON ETHAN A. BLIGHT Blight Investigations, Gainesville, FL ABSTRACT Misidentification of the American brown bear (Ursus arctos,
More informationAlpine Funds 2017 Tax Guide
Alpine s 2017 Guide Alpine Dynamic Dividend ADVDX 1/30/17 1/31/17 1/31/17 0.020000000 0.019248130 0.000000000 0.00000000 0.019248130 0.013842273 0.000000000 0.000000000 0.000751870 0.000000000 0.00 0.00
More informationAlpine Funds 2016 Tax Guide
Alpine s 2016 Guide Alpine Dynamic Dividend ADVDX 01/28/2016 01/29/2016 01/29/2016 0.020000000 0.017621842 0.000000000 0.00000000 0.017621842 0.013359130 0.000000000 0.000000000 0.002378158 0.000000000
More informationCrop Progress. Corn Mature Selected States [These 18 States planted 92% of the 2017 corn acreage]
Crop Progress ISSN: 00 Released October, 0, by the National Agricultural Statistics Service (NASS), Agricultural Statistics Board, United s Department of Agriculture (USDA). Corn Mature Selected s [These
More informationLABORATORY REPORT. If you have any questions concerning this report, please do not hesitate to call us at (800) or (574)
LABORATORY REPORT If you have any questions concerning this report, please do not hesitate to call us at (800) 332-4345 or (574) 233-4777. This report may not be reproduced, except in full, without written
More informationLABORATORY REPORT. If you have any questions concerning this report, please do not hesitate to call us at (800) or (574)
LABORATORY REPORT If you have any questions concerning this report, please do not hesitate to call us at (800) 332-4345 or (574) 233-4777. This report may not be reproduced, except in full, without written
More informationMeteorology 110. Lab 1. Geography and Map Skills
Meteorology 110 Name Lab 1 Geography and Map Skills 1. Geography Weather involves maps. There s no getting around it. You must know where places are so when they are mentioned in the course it won t be
More informationBlackRock Core Bond Trust (BHK) BlackRock Enhanced International Dividend Trust (BGY) 2 BlackRock Defined Opportunity Credit Trust (BHL) 3
MUNICIPAL FUNDS Arizona (MZA) California Municipal Income Trust (BFZ) California Municipal 08 Term Trust (BJZ) California Quality (MCA) California Quality (MUC) California (MYC) Florida Municipal 00 Term
More informationMINERALS THROUGH GEOGRAPHY
MINERALS THROUGH GEOGRAPHY INTRODUCTION Minerals are related to rock type, not political definition of place. So, the minerals are to be found in a variety of locations that doesn t depend on population
More informationextreme weather, climate & preparedness in the american mind
extreme weather, climate & preparedness in the american mind Extreme Weather, Climate & Preparedness In the American Mind Interview dates: March 12, 2012 March 30, 2012. Interviews: 1,008 Adults (18+)
More informationGrand Total Baccalaureate Post-Baccalaureate Masters Doctorate Professional Post-Professional
s by Location of Permanent Home Address and Degree Level Louisiana Acadia 19 13 0 3 0 3 0 0 0 Allen 5 5 0 0 0 0 0 0 0 Ascension 307 269 2 28 1 6 0 1 0 Assumption 14 12 0 1 0 1 0 0 0 Avoyelles 6 4 0 1 0
More informationOUT-OF-STATE 965 SUBTOTAL OUT-OF-STATE U.S. TERRITORIES FOREIGN COUNTRIES UNKNOWN GRAND TOTAL
Report ID: USSR8072-V3 Page No. 1 Jurisdiction: ON-CAMPUS IL Southern Illinois University - Carb 1 0 0 0 Black Hawk College Quad-Cities 0 0 1 0 John A Logan College 1 0 0 0 Rend Lake College 1 0 0 0 Aurora
More informationJAN/FEB MAR/APR MAY/JUN
QF 1.100 (Build 1010) Widget Publishing, Inc Page: 1 Circulation Breakdown by Issue Analyzed Nonpaid and Verified Paid Previous This Previous This Total Total issue Removals Additions issue issue Removals
More informationAn Analysis of Regional Income Variation in the United States:
Modern Economy, 2017, 8, 232-248 http://www.scirp.org/journal/me ISSN Online: 2152-7261 ISSN Print: 2152-7245 An Analysis of Regional Income Variation in the United States: 1969-2013 Orley M. Amos Jr.
More informationGrand Total Baccalaureate Post-Baccalaureate Masters Doctorate Professional Post-Professional
s by Location of Permanent Home Address and Degree Level Louisiana Acadia 26 19 0 6 1 0 0 0 0 Allen 7 7 0 0 0 0 0 0 0 Ascension 275 241 3 23 1 6 0 1 0 Assumption 13 12 0 1 0 0 0 0 0 Avoyelles 15 11 0 3
More informationMINERALS THROUGH GEOGRAPHY. General Standard. Grade level K , resources, and environmen t
Minerals through Geography 1 STANDARDS MINERALS THROUGH GEOGRAPHY See summary of National Science Education s. Original: http://books.nap.edu/readingroom/books/nses/ Concept General Specific General Specific
More informationInsurance Department Resources Report Volume 1
2014 Insurance Department Resources Report Volume 1 201 Insurance Department Resources Report Volume One 201 The NAIC is the authoritative source for insurance industry information. Our expert solutions
More informationOffice of Budget & Planning 311 Thomas Boyd Hall Baton Rouge, LA Telephone 225/ Fax 225/
Louisiana Acadia 20 17 3 0 0 0 Allen 2 2 0 0 0 0 Ascension 226 185 37 2 1 1 Assumption 16 15 1 0 0 0 Avoyelles 20 19 1 0 0 0 Beauregard 16 11 4 0 0 1 Bienville 2 2 0 0 0 0 Bossier 22 18 4 0 0 0 Caddo 91
More informationGIS use in Public Health 1
Geographic Information Systems (GIS) use in Public Health Douglas Morales, MPH Epidemiologist/GIS Coordinator Office of Health Assessment and Epidemiology Epidemiology Unit Objectives Define GIS and justify
More informationUnited States Geography Unit 1
United States Geography Unit 1 I WANT YOU TO STUDY YOUR GEORGAPHY Name: Period: Due Date: Geography Key Terms Absolute Location: Relative Location: Demographic Map: Population Density: Sun-Belt: Archipelago:
More informationNational Organization of Life and Health Insurance Guaranty Associations
National Organization of and Health Insurance Guaranty Associations November 21, 2005 Dear Chief Executive Officer: Consistent with prior years, NOLHGA is providing the enclosed data regarding insolvency
More informationInfant Mortality: Cross Section study of the United State, with Emphasis on Education
Illinois State University ISU ReD: Research and edata Stevenson Center for Community and Economic Development Arts and Sciences Fall 12-15-2014 Infant Mortality: Cross Section study of the United State,
More informationKS PUBL 4YR Kansas State University Pittsburg State University SUBTOTAL-KS
Report ID: USSR8072-V3 Page No. 1 Jurisdiction: ON-CAMPUS IL PUBL TCH DeVry University Addison 1 0 0 0 Eastern Illinois University 1 0 0 0 Illinois State University 0 0 2 0 Northern Illinois University
More informationFLOOD/FLASH FLOOD. Lightning. Tornado
2004 Annual Summaries National Oceanic and Atmospheric Administration National Environmental Satellite Data Information Service National Climatic Data Center FLOOD/FLASH FLOOD Lightning Tornado Hurricane
More informationRank University AMJ AMR ASQ JAP OBHDP OS PPSYCH SMJ SUM 1 University of Pennsylvania (T) Michigan State University
Rank University AMJ AMR ASQ JAP OBHDP OS PPSYCH SMJ SUM 1 University of Pennsylvania 4 1 2 0 2 4 0 9 22 2(T) Michigan State University 2 0 0 9 1 0 0 4 16 University of Michigan 3 0 2 5 2 0 0 4 16 4 Harvard
More informationPima Community College Students who Enrolled at Top 200 Ranked Universities
Pima Community College Students who Enrolled at Top 200 Ranked Universities Institutional Research, Planning and Effectiveness Project #20170814-MH-60-CIR August 2017 Students who Attended Pima Community
More informationA Summary of State DOT GIS Activities
A Summary of State DOT GIS Activities Prepared for the 2006 AASHTO GIS-T Symposium Columbus, OH Introduction This is the 11 th year that the GIS-T Symposium has conducted a survey of GIS activities at
More informationNon-iterative, regression-based estimation of haplotype associations
Non-iterative, regression-based estimation of haplotype associations Benjamin French, PhD Department of Biostatistics and Epidemiology University of Pennsylvania bcfrench@upenn.edu National Cancer Center
More informationDOWNLOAD OR READ : USA PLANNING MAP PDF EBOOK EPUB MOBI
DOWNLOAD OR READ : USA PLANNING MAP PDF EBOOK EPUB MOBI Page 1 Page 2 usa planning map usa planning map pdf usa planning map Printable USA Blank Map, USA Blank Map PDF, Blank US State Map. Thursday, 19
More informationMO PUBL 4YR 2090 Missouri State University SUBTOTAL-MO
Report ID: USSR8072-V3 Page No. 1 Jurisdiction: ON-CAMPUS IL American Intercontinental Universit 0 0 1 0 Northern Illinois University 0 0 4 0 Southern Illinois Univ - Edwardsvil 2 0 2 0 Southern Illinois
More informationAll-Time Conference Standings
All-Time Conference Standings Pac 12 Conference Conf. Matches Sets Overall Matches Team W L Pct W L Pct. Score Opp Last 10 Streak Home Away Neutral W L Pct. Arizona 6 5.545 22 19.537 886 889 6-4 W5 4-2
More information112th U.S. Senate ACEC Scorecard
112th U.S. Senate ACEC Scorecard HR 658 FAA Funding Alaska Alabama Arkansas Arizona California Colorado Connecticut S1 Lisa Murkowski R 100% Y Y Y Y Y Y Y Y Y S2 Mark Begich D 89% Y Y Y Y N Y Y Y Y S1
More informationStatistical Methods for Data Mining
Statistical Methods for Data Mining Kuangnan Fang Xiamen University Email: xmufkn@xmu.edu.cn Linear Model Selection and Regularization Recall the linear model Y = 0 + 1 X 1 + + p X p +. In the lectures
More informationStem-and-Leaf Displays
3.2 Displaying Numerical Data: Stem-and-Leaf Displays 107 casts in your area? (San Luis Obispo Tribune, June 15, 2005). The responses are summarized in the table below. Extremely 4% Very 27% Somewhat 53%
More informationFungal conservation in the USA
The following supplements accompany the article Fungal conservation in the USA Jessica L. Allen*, James C. Lendemer *Corresponding author: jlendemer@nybg.org Endangered Species Research 28: 33 42 (2015)
More informationIntroducing North America
Introducing North America I. Quick Stats Includes U.S. & Canada U.S consists of 50 States Federal Government Democracy 4 th in world w/ land area 3 rd in population Economic leader of free world II. Major
More informationEvidence for increasingly extreme and variable drought conditions in the contiguous United States between 1895 and 2012
Evidence for increasingly extreme and variable drought conditions in the contiguous United States between 1895 and 2012 Sierra Rayne a,, Kaya Forest b a Chemologica Research, 318 Rose Street, PO Box 74,
More informationOctober 2016 v1 12/10/2015 Page 1 of 10
State Section S s Effective October 1, 2016 Overview The tables list the Section S items that will be active on records with a target date on or after October 1, 2016. The active on each item subset code
More informationOffice of Budget & Planning 311 Thomas Boyd Hall Baton Rouge, LA Telephone 225/ Fax 225/
Louisiana Acadia 25 19 4 2 0 0 Allen 8 7 1 0 0 0 Ascension 173 143 26 1 0 3 Assumption 14 12 2 0 0 0 Avoyelles 51 41 9 0 0 1 Beauregard 18 14 3 0 0 1 Bienville 5 0 4 0 1 0 Bossier 28 27 0 1 0 0 Caddo 95
More informationChapter 11 : State SAT scores for 1982 Data Listing
EXST3201 Chapter 12a Geaghan Fall 2005: Page 1 Chapter 12 : Variable selection An example: State SAT scores In 1982 there was concern for scores of the Scholastic Aptitude Test (SAT) scores that varied
More informationGreen Building Criteria in Low-Income Housing Tax Credit Programs Analysis
Green Building Criteria in Low-Income Housing Tax Credit Programs 2010 Analysis www.globalgreen.org September 2010 Green Building Criteria in State Low-Income Housing Tax Credit Programs 2 Introduction
More informationThis project is supported by a National Crime Victims' Right Week Community Awareness Project subgrant awarded by the National Association of VOCA
TABLE OF CONTENTS ACTIVITY PAGES Coloring Pages Younger Children (6) Coloring Pages Older (2) Connect the Dots (1 English, 1 Spanish) Word Search 1 (1 English, 1 Spanish) Younger Children Word Search 2
More informationPackage ZIM. R topics documented: August 29, Type Package. Title Statistical Models for Count Time Series with Excess Zeros. Version 1.
Package ZIM August 29, 2013 Type Package Title Statistical Models for Count Time Series with Excess Zeros Version 1.0 Date 2013-06-15 Author Ming Yang, Gideon K. D. Zamba, and Joseph E. Cavanaugh Maintainer
More informationA GUIDE TO THE CARTOGRAPHIC PRODUCTS OF
A GUIDE TO THE CARTOGRAPHIC PRODUCTS OF THE FEDERAL DEPOSITORY LIBRARY PROGRAM (FDLP) This guide was designed for use as a collection development tool by map selectors of depository libraries that participate
More informationSt. Luke s College Institutional Snapshot
1. Student Headcount St. Luke s College Institutional Snapshot Enrollment # % # % # % Full Time 139 76% 165 65% 132 54% Part Time 45 24% 89 35% 112 46% Total 184 254 244 2. Average Age Average Age Average
More informationDivision I Sears Directors' Cup Final Standings As of 6/20/2001
1 Stanford (Cal.) 1359 2 90 9 75 20 62.5 0 0 0 0 0 0 3 75 1 100 5 60 8 76 4 80 0 0 2 70 2 California-Los Angeles 1138 0 0 5 78.5 17 67 0 0 0 0 0 0 2 90 9 50 5 60 2 0 33 48.5 2 70 1 100 3 Georgia 890.5
More informationEarl E. ~rabhl/ This report is preliminary and has not been reviewed for conformity with U.S. Geological Survey editoral standards GEOLOGICAL SURVEY
UNITED STATES DEPARTMENT OF THE INTERIOR GEOLOGICAL SURVEY Minimum landslide damage in the United States, 1973-1983 by Earl E. ~rabhl/ Open-File Report 84-486 This report is preliminary and has not been
More informationChapter 5 Linear least squares regression
Chapter 5 Linear least squares regression Consider first simple linear regression. For now, the model of interest is only the model of the conditional expectations; the distribution of the residuals is
More informationDirect Selling Association 1
Total Number of Firms 1,100 104 58 46 14 41 22 27 44 60 1 U.S. Sales Volume & Growth 1 Average Retail Sales per Firm ($thousands) 2015 $32,836 $210,572 $320,820 $69,556 $1,041 $11,788 $100,216 $721,897
More informationPhysical Features of Canada and the United States
I VIUAL Physical Features of Canada and the United tates 170 ARCTIC OCA Aleutian s 1 Bering ea ALAKA Yukon R. Mt. McKinley (20,320 ft. 6,194 m) Gulf of Alaska BROOK RAG RAG Queen Charlotte s R Vancouver
More informationPhysical Features of Canada and the United States
Physical Features of Canada and the United tates 170 ARCTIC OCA Aleutian s 1 1 Bering ea ALAKA Yukon R. Mt. McKinley (20,320 ft. 6,194 m) Gulf of Alaska BROOK RAG RAG Queen Charlotte s R Vancouver O C
More informationSummary of Terminal Master s Degree Programs in Philosophy
Summary of Terminal Master s Degree Programs in Philosophy Faculty and Student Demographics All data collected by the ican Philosophical Association. The data in this publication have been provided by
More informationPHYSICS BOWL - APRIL 22, 1998
AAPT/Metrologic High School Physics Contest PHYSICS BOWL - APRIL 22, 1998 40 QUESTIONS 45 MINUTES This contest is sponsored by the American Association of Physics Teachers (AAPT) and Metrologic Instruments
More informationCCC-A Survey Summary Report: Number and Type of Responses
CCC-A Survey Summary Report: Number and Type of Responses Suggested Citation: American Speech-Language-Hearing Association. (2011). 2011 Membership survey. CCC-A survey summary report: Number and type
More informationCrop / Weather Update
Crop / Weather Update Corn Crop Condition Percent of Acreage Rated Good or Excellent 85 80 75 70 65 60 55 50 45 As of September 9, USDA rates the crop at 68% good to excellent. The rating is up one point
More informationIf you have any questions concerning this report, please feel free to contact me. REPORT OF LABORATORY ANALYSIS
#=CL# LI USE: FR - JILL LAVOIE LI OBJECT ID: August 22, 2014 Jill Lavoie Merrimack Village District - UCMR3 2 Greens Pond Road Merrimack, NH 03054 RE: Dear Jill Lavoie: Enclosed are the analytical results
More informationState Section S Items Effective October 1, 2017
State Section S Items Effective October 1, 2017 Overview The tables list the Section S items that will be active on records with a target date on or after October 1, 2017. The active item on each item
More informationBOWL - APRIL 27, QUESTIONS 45 MINUTES
PHYSICSBOWL AAPT/Metrologic High School Physics Contest PHYSICS BOWL - APRIL 27, 1995 40 QUESTIONS 45 MINUTES This contest is sponsored by the American Association of Physics Teachers (AAPT) and Metrologic
More informationNational Council for Geographic Education Curriculum & Instruction Committee Geography Club Submitted by: Steve Pierce
National Council for Geographic Education Curriculum & Instruction Committee Geography Club Submitted by: Steve Pierce stevepierce@charter.net Ninth Month Activities Geography for Life: National Geography
More informationIf you have any questions concerning this report, please feel free to contact me. REPORT OF LABORATORY ANALYSIS
#=CL# LIMS USE: FR - JILL LAVOIE LIMS OBJECT ID: July 16, 2015 Jill Lavoie Merrimack Village District - UCMR3 2 Greens Pond Road Merrimack, NH 03054 RE: Dear Jill Lavoie: Enclosed are the analytical results
More informationThe Heterogeneous Effects of the Minimum Wage on Employment Across States
The Heterogeneous Effects of the Minimum Wage on Employment Across States Wuyi Wang a, Peter C.B. Phillips b, Liangjun Su c a Institute for Economic and Social Research, Jinan University b Yale University,
More information2011 NATIONAL SURVEY ON DRUG USE AND HEALTH
2011 NATIONAL SURVEY ON DRUG USE AND HEALTH PERSON-LEVEL SAMPLING WEIGHT CALIBRATION Prepared for the 2011 Methodological Resource Book Contract No. HHSS283200800004C RTI Project No. 0211838.207.004 Phase
More information