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Computing and Data Analysis for Environmental Applications Assignment help


We at Global web tutors provide expert help for Computing and Data Analysis for Environmental Applications assignment or Computing and Data Analysis for Environmental Applications homework. Our Computing and Data Analysis for Environmental Applications online tutors are expert in providing homework help to students at all levels. Please post your assignment at support@globalwebtutors.com to get the instant Computing and Data Analysis for Environmental Applications homework help. Computing and Data Analysis for Environmental Applications online tutors are available 24/7 to provide assignment help as well as Computing and Data Analysis for Environmental Applications homework help.

Topics for ATMS 305   Computing and Data Analysis

  • statistical treatment, graphical representation of atmospheric sciences data, methods of interpolation
  • linear correlations, nonlinear correlations, data analysis, modeling data

Topics for Computing and Data Analysis for Environmental Applications  

  • Descriptive Statistics  , Probablility , Joint Probability, Independence, Combinatorial Methods for Deriving Probabilities , Conditional Probability , Baye's Theorem  , Random Variables , Probability Distributions  , Expectation, Functions of a Random Variable , Risk  , Some Common Probability Distributions, Multivariate Probability
  • Functions of Many Random Variables  , Populations Samples  , Estimation  , Confidence Intervals  , Testing Hypotheses about a Single Population , Testing Hypotheses about Two Populations , Small Sample Statistics , Analysis of Variance  , Analysis of Variance  , Multifactor Analysis of Variance  , Linear Regression  , Analyzing Regression Result, averages
  • variances, standard deviation, errors  propagation, error propagation, multi-dimensional problems, Binomial distributions , Poisson distributions , Gaussian distributions , Concepts of probability, confidence intervals limits, hypothesis testing
  • Optimisation techniques , maximum-likelihood techniques, multivariate analysers , context of data mining, Fisher discriminants, multi-layer perceptron , artificial neural networks, decision trees , genetic algorithms
  • problems solving techniques,algorithm design,data types and operators,conditional and repetitive control flow,file access,data visualisation,code optimisation,arrays/matrices,vectorisation

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