Statistics Assignment Help | Statistics Homework Help | Statistics Online Tutors
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Some of the homework help topics include :
- Stochastic Processes ,Linear Modelling: Theory and Applications ,Sampling Surveys ,Time Series
- Modern Statistical Prediction and Machine Learning ,Game Theory ,Design and Analysis of Experiments
- Reproducible and Collaborative Statistical Data Science
- Descriptive statistics: diagrams and measures, Planning and design of statistical studies,
- Randomisation-based inference: Confidence Intervals and Hypothesis Testing.
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Descriptive Statistics are the methods used to organize, summarize and present data in a informative and convenient manner. These methods include Graphical & Numerical Techniques.
Inferential Statistics are also a kind methods or a set of methods which is used to derive conclusions or inferences for a given problem.
Statistical Inference is a process of derive an estimate ,decision,or prediction to a given problem. Statistical inference which is ''measures of reliability'' i.e. confidence level and significance level.
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Topics like Variance, ANOVA , AP Statistics , Bayesian Learning, Bernoulli Distributions, Beta Distributions, Chi-Square Distributions, Continuous Time Markov Chains & models , Correlations, Discrete Time are really complex & the assignment help on these topics is really helpful if you are struggling with the complex problems on topics including Exponential Distributions, Frequency Distribution Tables, Gamma Distributions, Geometric Distributions, Hypergeometric Distributions, Inferential Statistics, Least Squares Regression, Normal Distributions.
Statistics assignment help is the need of university students pursuing degree programs in various discipline. Statistics Assignment is a nightmare for students if the thorough statistical concepts are missing. You need to have sound knowledge for statistical softwares & knowledge of analysis in order to solve the complex Statistics assignments.
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Complex topics include :
R and Excel for statistical analysis ,statistical methodology ,Probability and Distributions of Random Variables
Null and Alternative Hypotheses, Poisson Distributions, P-Values Regression Analysis, Sample Bias and Distribution, SPSS, Statistical Graphs, Student T-Distribution, T-Tests, Uniform Distributions, Z-Scores, Resampling methods.
Interpretation of results of statistical procedures in addition to problem solving ,data analysis,statistical computing
Null hypothesis test, z-test, t-test, chi-squared test, fractional factorials, fractional blocking, aliasing, orthogonal arrays, industrial split-plot designs, Response-surface method, Sequential Monte Carlo
Model Selection and Logistic Regression, Market Model, Bayesian Inference, Dynamic Linear Models, Dynamic Regression Models, Posterior Simulation and AR model Stochastic Volatility Models, Latent Variable Models, FFBS.
Gaussian distributions, decision trees, Maximum likelihood, asymptotic theory, nuisance parameters, score tests, Wald tests, Multivariate quadratic forms.
Standard distributions, Sampling distributions, Simple linear regression, Non-parametric tests, Wilcoxon signed-rank test
- Probability intervals ,Conjugate priors ,Frequentist significance tests ,bootstrapping ,Computation, simulation, and visualization using R
Categorical data analysis, log-linear models, nonparametric methods ,MegaStat , Multilevel Longitudinal Modelling ,Nonlinear Dynamics Analysis, SPSS Amos- SEM ,Reliability Theory ,SPSS- Factor Analysis, Black Scholes Theory , Excel , Minitab, Survey Methodology .
Risk Modelling, Derivatives Modelling , Mathematical Programming Algorithms, EViews ,LISREL boxplot,Non-Gaussian Models .
- Time Series, Dynamic Regression Models , Posterior Simulation and AR model , Markov-Switching Models ,Latent Variable Models ,Kalman Filter, FFBS ,Savage's theory ,coherence ,Arrow's impossibility theorem ,consensus, violations of Savage's postulate.
Few Topics are:
- data sources
- sample surveys and administrative data
- the legal and ethical framework of official statistics
- introduction to demography
- the collection and analysis of health
- social and economic data
- data visualisation
- presentation of spatial data
- data matching and integration