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 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,Randomisationbased inference: interval and hypothesis test
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Statistics is collecting information from the data usually the facts.The study of statistics involves math and which relies upon the calculations of numbers but it also relies heavily on how the numbers are chosen and how the statistics are interpreted.
Statistics is a kind of tool that is needed in order to react intelligently to the information we hear or read,used to create an understanding from a given set of numbers.
Statistics are often presented in an effort to add credibility to an argument .
Descriptive Statistics are the methods used to organise, summarize and present data in a informative and convenient manner. These methods include some techniques that are:
>>Graphical Techniques
>>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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Variance, ANOVA, AP Statistics, Bayesian Learning, Bernoulli Distributions, Beta Distributions, Binomial Distributions, ChiSquare Distributions, Continuous Time Markov Chains, Correlations, Discrete Time Markov Chains and Models, Probability, Exponential Distributions, Frequency Distribution Tables, Gamma Distributions, Geometric Distributions, Hypergeometric Distributions, Inferential Statistics, Least Squares Regression,Normal Distributions.

R and Excel for statistical analysis ,statistical methodology ,Measuring and Describing Variables ,Probability and Distributions of Random Variables ,Confidence Intervals and Hypothesis Testing

Null and Alternative Hypotheses, Permutations and Combinations, Poisson Distributions, PValues Regression Analysis, Sample Bias and Distribution, SPSS, Statistical Graphs, Stochastic Processes and Modeling, Student TDistribution, TTests, Uniform Distributions, ZScores, Applied probability, Resampling methods.

Descriptive statistical techniques, regression, design of sample surveys and experiments, basic probability, statistical inference ,
Interpretation of results of statistical procedures in addition to problem solving ,data analysis,Minitab ,statistical computing 
Null hypothesis test, ztest, ttest, chisquared test, fractional factorials, fractional blocking, aliasing, orthogonal arrays, industrial splitplot designs, Responsesurface method, Sequential Monte Carlo, NonGaussian Models .
Time Series, Model Selection and Logistic Regression, Market Model, Bayesian Inference, Dynamic Linear Models, Dynamic Regression Models, Posterior Simulation and AR model Stochastic Volatility Models, MarkovSwitching Models, Latent Variable Models, FFBS. 
Binomial, Poisson, Gaussian distributions, decision trees, Maximum likelihood, asymptotic theory, nuisance parameters, score tests, Wald tests, Multivariate normal distribution, quadratic forms, general linear model
Standard distributions, Statistical inference, Sampling distributions, Simple linear regression, Nonparametric tests, Wilcoxon signedrank test  Bayesian inference with known priors, probability intervals ,Conjugate priors ,Frequentist significance tests and confidence intervals ,Resampling methods: bootstrapping ,Linear regression ,Computation, simulation, and visualization using R

Categorical data analysis, loglinear 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,NonGaussian Models .  Time Series,, Dynamic Regression Models , Posterior Simulation and AR model ,Stochastic Volatility Models, MarkovSwitching Models ,Latent Variable Models ,Kalman Filter, FFBS ,Savage's theory ,coherence ,Arrow's impossibility theorem ,consensus, violations of Savage's postulate ,Imprecise Probability [IP] theory