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Applied statistics are mainly concerned with the application of theoretical statistics in different-different field such as Government sectors, Business Clinical Trials, and many more. It deals with the theoretical statistics related theorems and concepts. A professional statistician is one who is responsible for the production of statistics. Applied Statistics deals with the various concepts viz. Biostatistics, Chebychev’s inequality, EVPI & EVSI, Hidden Markov Model, Robotics, Chemometrics, Statistical inference, Bayesian statistics, Approximation theory, etc.

There are various Statistical software that uses to perform the analysis task, including DDXL, SPSS – PASW, STATA, EPIINFO, ARCGIS 9.2, WEKA, GRETL, G*POWER, MPLUS, Stat Disk, Statpro, MINITAB, SHAZAM WINBUGS, etc. Applied statistics mainly concerned with the data theory. In designing of the experiments, it deals with the preparation of the data. Applied statistics provides its applications in the various areas such as medicine, engineering, marketing, biology, data mining, education, economics, biostatistics techniques, predictive analysis, public health, marketing, etc. Applied statistics can be divided into two categories:

  • Descriptive statistics: it is mainly used to describe the fundamental characteristics of the data. Large datasets cannot be used for drawing the conclusions directly. So, for drawing the conclusions about large datasets, a user used descriptive statistics to define the fundamental characteristics of the data. It majorly involves graphs/charts and summary tables. Graphs/charts include line plot, pie chart, histogram, etc. and summary tables include standard deviation, mean, media, mode, etc.
  • Inferential Statistics: It draws the conclusions about the whole population on the basis of sample information. Its main goal is to analyze the sample data and then make the inferences about the characteristics of the population.

Statistical computing mainly concerned with the computational methods which leads to enable the statistical methods.  Statistical computing deals with the use of linear algebra, numerical integration and simulation in statistical computation. It includes computer graphics, s/w engg, numerical analysis and database methodology.

A well known software of Applied Statistics which is named as R comes with a variety of predefined graphical techniques. It acts as the software environment for statistical computing and a user can easily add the new techniques in it because it is extensible.

In R^d, Selection of the convex hull of random points by following the normal distribution is termed as a Gaussian polytope. In R^d, we can establish the estimate variance of i-faces and i-th volume by using the means of Efron-Stein jackknife inequality or else we can use another formula of Blaschke-Petkantschin type. Gaussian polytope is based on the cumulants due to this reason, it porivdes the new concentration inequalities, moment bounds, central limit theorems, etc.

Fiducial inference is used to draw the conclusions from the given samples of the data. It is a type of statistical inference which plays an imperative role in the history of statistics. In statistical methodology, various current research is connected with the Fiducial inference, some closely or some explicitly. It is designed for solve the problems of Bayesian approach.

Graphical LASSO is an effective approach which enables user to know about the structure of the GGM by maximizing the log likelihood of the data. We can summarize the dependencies between variables with the Graphical model and Graphical LASSO takes the observation form the multivariate Gaussian distribution to estimate the precision matrix. Problems of Graphical LASSO can be solved by using the GGM structure whereas GGM stands for Gaussian graphical model. To estimate the sparse inverse covariance matrix, Graphical LASSO model provides a MATLAB implementation which is:

minimize    tr( Theta * S ) – logdet( Theta )  + ρ * || Theta ||1

Statistical Surveys mainly concerned with the assembling of information about the items in population. It involves Survey Design, Data collection, Estimation and Sample collection. During a sample survey, there are two types of major error occur, including:

  • Sampling error
  • Non-sampling error

Advanced regression methods mainly deal with the various concepts such as SPline regression, regularization methods, linear models, ridge regression, etc. There is a problem arise with the linear regression that outliers can affect this, but RANSAC (Advanced Regression Methods) is the robust approach of Advanced Regression Methods which is not affected by outliers.

Moreover, Applied Statistics is a vast field and it involves various concepts for study. Students that pursuing their higher studies in this field deals with the various major concepts along with the above explained concepts which includes Kernel density estimation, Lyapunov CLT, Martingale difference CLT, Reliability engineering, Grid-based estimators, Decision tree, Statistical misinterpretation, Statistical methods in public health, and many more.

Some PG level students and PhD level students always remain in the search of the Advanced Concepts of the Applied Statistics to make their effective dissertation. Some of the Advanced Concepts that take place in the Applied Statistics are as follows:

  • Exploratory Multivariate Data Analysis
  • Multiple Correspondence Analysis
  • Factorial analysis for mixed data
  • Quantitative Reasoning
  • Regression and Multivariate Data Analysis
  • Applied Bayesian Statistics
  • Multiple factor analysis
  • Regression Models
  • Applied Nonparametric Statistics

Students who are in the search of Applied Statistics Assignment can contact us. We have a team of proficient experts especially for this field. They provide the assignment to students on all topics of the Applied Statistics such as Bootstrap, Generalized additive models, Categorical Analysis, Statistical methods in genetics, Statistical methods in Biomedical Research, etc. With these topics they provide the assignment on the advanced and complex topics of Applied Statistics on which students find to b every difficulty. Some of advanced and complex topics are Categorical Data Analysis, SEM, Linear Regression Analysis, LGM, LGCM, Logistic Regression, Survival and Event-Count Models, Factor and Cluster Analysis, Growth Mixture Modeling, Time Series Analysis, Multilevel and Mixed-Effects Modeling, etc. Some benefits that you can get by choosing us are given as:

  • Work offered at affordable rates
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computing environments, statistical packages, descriptive statsitics, basic inferential methods, confidence intervals, chi-square tests, linear models, regression, ANOVA models, specification, assumption, fitting, diagnostics, selection, testing, interpretation, regression analysis, involves modeling data, diagnostic methods, statistical inference, applied statistics course, data analysis, computing, communicating, statistics course, 


Finite Mixture Models – Latent Class Analysis Macready
  • Univariate Distributions – Structured and Unstructured Mixtures, Multivariate Distributions – Structured and Unstructured Mixtures, SEM – Theoretical Foundations, Mixtures in SEM ‐ Estimation, Interpretation, Application, LGCM – Theoretical Foundations
  • Growth Mixture Modeling– Estimation, Interpretation,, Application, Applications and/or Methodological Extensions, IRT – Theoretical Foundations, Nov Mixtures in IRT – Model Specification, Estimation, Interpretation, Latent DIF & Model‐Based Standard Setting –, Applications in Psychometrics, Nov Introduction to DCM –Theoretical Foundations, Applications – Estimation
  • Interpretation, Computer Software, Applications and/or Method, Data, samples and populations, Graphical and numerical descriptions of data, Association between variables, Probability, probability distributions and the, normal distribution, Sampling, experiments, and observational, studies, Introduction to inferential statistics, Confidence interval for proportions
  • Hypothesis tests for proportions, Using Technology, Inferences for multiple proportions, Confidence intervals and hypothesis, Inference for multiple means; ANOVA, Inference for linear regression, sampling distributions, probability, confidence intervals, t tests, ANOVA, correlation, regression, nonparametric statistics, data transformation, null hypothesis significance testing 
Topics for Applied Statistics 
  • Data collection and interpretation, Probability models, Basic statistical methods, Linear models, Further probability and inference, Further applications of statistics, Time series and index numbers, Survey sampling and estimation, Data, samples
  • populations, Graphical numerical descriptions of data, Association between variables, Probability, probability distributions , normal distribution, Sampling, experiments, observationalstudies, inferential statistics, Confidence interval for proportions
  • Hypothesis tests for proportions, Inferences for multiple proportions, Confidence intervals , hypothesis, Inference for multiple means, Inference for linear regression
  • Linear and mixed models
  • Generalised linear models
  • Statistical Inference
  • Statistical Programming
  • Programming; Graphics and visualisation
  • Computational Statistics
  • Non-linear and non-parametric models
  • Bootstrap; Hidden Markov Model
  • Bayes Methods
  • Prior elicitation
  • Bayesian non-parametrics
  • Approximation methods
  • Data Mining and Machine Learning
  • Unsupervised Machine Learning
  • kernel and Ensemble methods
  • Statistical Consulting
  • SAS Programming and Applied Statistics
  • Probability and Statistical Theory
  • Statistical Design of Analysis of Experiments
  • Applied Regression Analysis
  • Stochastic Processes
  • Categorical Data Analysis
  • Applied Survival Analysis
  • Applied Multivariate Statistical Analysis
  • Advanced Statistical Theory
  • Advanced Biostatistics
  • Quantitative Methods in Bioinformatics
  • Topics in Statistics and Biostatistics
  • Regression modeling extended to categorical data.
  • Logistic regression.
  • Loglinear models.
  • Generalized linear models.
  • Discriminant analysis.
  • Categorical data models from information retrieval and Internet modeling
  • Regression and prediction
  • Elements of the analysis of variance
  • Bootstrap and cross-validation.
  • Emphasis is on conceptual rather than theoretical understanding.
  • Applications to social/biological sciences.
  • Clustering, Biclustering and spectral clustering.
  • Singular value decomposition
  • Nonnegative decomposition
  • Generalizations
  • Plaid model
  • Aspect model and additive clustering
  • Correspondence analysis
  • Rasch model
  • Independent component analysis.
  • Page rank
  • Hubs and authorities.
  • Probabilistic latent semantic indexing
  • Statistical tools for modern data analysis

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