Dr. SaMeH S. Ahmed

Associate Prof. of Environmental Engineering

Multivariate Stat



Multivariate statistics is a form of statistics encompassing the simultaneous observation and analysis of more than one outcome variable. The application of multivariate statistics is multivariate analysis.

Multivariate statistics concerns understanding the different aims and background of each of the different forms of multivariate analysis, and how they relate to each other. The practical implementation of multivariate statistics to a particular problem may involve several types of univariate and multivariate analyses in order to understand the relationships between variables and their relevance to the actual problem being studied.

In addition, multivariate statistics is concerned with multivariate probability distributions, in terms of both

  • how these can be used to represent the distributions of observed data;
  • how they can be used as part of statistical inference, particularly where several different quantities are of interest to the same analysis.

Certain types of problem involving multivariate data, for example simple linear regression and multiple regression, are NOT usually considered as special cases of multivariate statistics because the analysis is dealt with by considering the (univariate) conditional distribution of a single outcome variable given the other variables.

There are many different models, each with its own type of analysis:

  1. Multivariate analysis of variance (MANOVA) extends the analysis of variance to cover cases where there is more than one dependent variable to be analyzed simultaneously; see also MANCOVA.
  2. Multivariate regression attempts to determine a formula that can describe how elements in a vector of variables respond simultaneously to changes in others. For linear relations, regression analyses here are based on forms of the general linear model. Note that Multivariate regression is distinct from Multivariable regression, which has only one dependent variable.
  3. Principal components analysis (PCA) creates a new set of orthogonal variables that contain the same information as the original set. It rotates the axes of variation to give a new set of orthogonal axes, ordered so that they summarize decreasing proportions of the variation.
  4. Factor analysis is similar to PCA but allows the user to extract a specified number of synthetic variables, fewer than the original set, leaving the remaining unexplained variation as error. The extracted variables are known as latent variables or factors; each one may be supposed to account for covariation in a group of observed variables.
  5. Canonical correlation analysis finds linear relationships among two sets of variables; it is the generalised (i.e. canonical) version of bivariate correlation.
  6. Redundancy analysis (RDA) is similar to canonical correlation analysis but allows the user to derive a specified number of synthetic variables from one set of (independent) variables that explain as much variance as possible in another (independent) set. It is a multivariate analogue ofregression.
  7. Correspondence analysis (CA), or reciprocal averaging, finds (like PCA) a set of synthetic variables that summarise the original set. The underlying model assumes chi-squared dissimilarities among records (cases).
  8. Canonical (or "constrained") correspondence analysis (CCA) for summarising the joint variation in two sets of variables (like redundancy analysis); combination of correspondence analysis and multivariate regression analysis. The underlying model assumes chi-squared dissimilarities among records (cases).
  9. Multidimensional scaling comprises various algorithms to determine a set of synthetic variables that best represent the pairwise distances between records. The original method is principal coordinates analysis (PCoA; based on PCA).
  10. Discriminant analysis, or canonical variate analysis, attempts to establish whether a set of variables can be used to distinguish between two or more groups of cases.
  11. Linear discriminant analysis (LDA) computes a linear predictor from two sets of normally distributed data to allow for classification of new observations.
  12. Clustering systems assign objects into groups (called clusters) so that objects (cases) from the same cluster are more similar to each other than objects from different clusters.
  13. Recursive partitioning creates a decision tree that attempts to correctly classify members of the population based on a dichotomous dependent variable.
  14. Artificial neural networks extend regression and clustering methods to non-linear multivariate models.
  15. Statistical graphics such as tours, parallel coordinate plots, scatterplot matrices can be used to explore multivariate data.


taken from:

http://en.wikipedia.org/wiki/Multivariate_statistics


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Mining Engineering


Mining engineering is an engineering discipline that involves practice, theory, science, technology, and the application of extracting and processing minerals from a naturally occurring environment. Mining engineering also includes processing minerals for additional value.

Environmental Engineering

Environmental engineers are the technical professionals who identify and design solutions for    environmental problems. Environmental engineers provide safe drinking water, treat and properly dispose of wastes, maintain air quality, control water pollution, and remediate sites contaminated due to spills or improper disposal of hazardous substances. They monitor the quality of the air, water, and land. And, they develop new and improved means to protect the environment.

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Assuit University (Home University), Egypt


Imperial College, London, UK


Faculty of Engineering, Al-Mergeb University, Libya


King Saud University, KSA

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Course 2016/17-1


  1. Computer Applications in Surveying  CE 473
  2. Surveying 1 CE 370
  3. Photogrammetry CE 474
  4. Surveying II  CE 371
  5. Design I  (round 4) CE 498




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Member of the Editorial Board: " Journal of Water Resources and Ocean Sciences"  2013

https://www.researchgate.net/profile/Sameh_Ahmed5/?ev=hdr_xprf

Participating in The Third International Conference on Water, Energy and Environment,(ICWEE) 2015 - American University of Sharjah, UAE 24-26 March 2015 with a Paper and Poster

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CE 370 Course

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First midterm exam


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CE473 Course

Computer Applications in Surveying

CE 473



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2015-2016-2 - Photogrammetry  CE474




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CE 360: Environmental Engineering 1

37-2

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GE 306 Course

Engineering Report Writing


GE 306: Engineering Report Writing

CE 499 Course

Senior Design 2 - CE 499

Meeting on 14-4-2015

Second Best paper from Senior Design Projects in 2015

Paper title:

Evaluation of Groundwater Quality Parameters using Multivariate Statistics- a case Study of Majmaah, KSA

Students:

Abdullah A. Alzeer

Husam K. Almubark

Maijd M. Almotairi

CE 360-Summer Course

Environmental Engineering I


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Engineering Practice

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Civil and Environmental Engineering Department
College of Engineering, Majmaah University
Majmaah, P.O. 66, 11952, KSA

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Properties and Strength of Materials 1

CE 212

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Soil Mechanics and Foundation Engineering

CE 311

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